AutoMLSearch for time series problems#
In this guide, we’ll show how you can use EvalML to perform an automated search of machine learning pipelines for time series problems. Time series support is still being actively developed in EvalML so expect this page to improve over time.
But first, what is a time series?#
A time series is a series of measurements taken at different moments in time (Wikipedia). The main difference between a time series dataset and a normal dataset is that the rows of a time series dataset are ordered chronologically, where the relative time between rows is significant. This relationship between the rows does not exist in non-time series datasets. In a non-time-series dataset, you can shuffle the rows and the dataset still has the same meaning. If you shuffle the rows of a time series dataset, the relationship between the rows is completely different!
What does AutoMLSearch for time series do?#
In a machine learning setting, we are usually interested in using past values of the time series to predict future values. That is what EvalML’s time series functionality is built to do.
Loading the data#
In this guide, we work with daily minimum temperature recordings from Melbourne, Austrailia from the beginning of 1981 to end of 1990.
We start by loading the temperature data into two splits. The first split will be a training split consisting of data from 1981 to end of 1989. This is the data we’ll use to find the best pipeline with AutoML. The second split will be a testing split consisting of data from 1990. This is the split we’ll use to evaluate how well our pipeline generalizes on unseen data.
[2]:
import pandas as pd
from evalml.demos import load_weather
X, y = load_weather()
Number of Features
Categorical 1
Number of training examples: 3650
Targets
10.0 1.40%
11.0 1.40%
13.0 1.32%
12.5 1.21%
9.0 1.21%
...
23.4 0.03%
19.6 0.03%
0.5 0.03%
1.8 0.03%
21.6 0.03%
Name: count, Length: 229, dtype: object
[3]:
train_dates, test_dates = X.Date < "1990-01-01", X.Date >= "1990-01-01"
X_train, y_train = X.ww.loc[train_dates], y.ww.loc[train_dates]
X_test, y_test = X.ww.loc[test_dates], y.ww.loc[test_dates]
Visualizing the training set#
[4]:
import plotly.graph_objects as go
[5]:
data = [
go.Scatter(
x=X_train["Date"],
y=y_train,
mode="lines+markers",
name="Temperature (C)",
line=dict(color="#1f77b4"),
)
]
# Let plotly pick the best date format.
layout = go.Layout(
title={"text": "Min Daily Temperature, Melbourne 1980-1989"},
xaxis={"title": "Time"},
yaxis={"title": "Temperature (C)"},
)
go.Figure(data=data, layout=layout)
Fixing the data#
Sometimes, the datasets we work with do not have perfectly consistent DateTime columns. We can use the TimeSeriesRegularizer
and TimeSeriesImputer
to correct any discrepancies in our data in a time-series specific way.
To show an example of this, let’s create some discrepancies in our training data. We’ll also add a couple of extra columns in the X DataFrame to highlight more of the options with these tools.
[6]:
X["Categorical"] = [str(i % 4) for i in range(len(X))]
X["Categorical"] = X["Categorical"].astype("category")
X["Numeric"] = [i for i in range(len(X))]
# Re-split the data since we modified X
X_train, y_train = X.loc[train_dates], y.ww.loc[train_dates]
X_test, y_test = X.loc[test_dates], y.ww.loc[test_dates]
[7]:
X_train["Date"][500] = None
X_train["Date"][1042] = None
X_train["Date"][1043] = None
X_train["Date"][231] = pd.Timestamp("1981-08-19")
X_train.drop(1209, inplace=True)
X_train.drop(398, inplace=True)
y_train.drop(1209, inplace=True)
y_train.drop(398, inplace=True)
With these changes, there are now NaN values in the training data that our models won’t be able to recognize, and there is no longer a clear frequency between the dates.
[8]:
print(f"Inferred frequency: {pd.infer_freq(X_train['Date'])}")
print(f"NaNs in date column: {X_train['Date'].isna().any()}")
print(
f"NaNs in other training data columns: {X_train['Categorical'].isna().any() or X_train['Numeric'].isna().any()}"
)
print(f"NaNs in target data: {y_train.isna().any()}")
Inferred frequency: None
NaNs in date column: True
NaNs in other training data columns: False
NaNs in target data: False
Time Series Regularizer#
We can use the TimeSeriesRegularizer
component to restore the missing and NaN DateTime values we’ve created in our data. This component is designed to infer the proper frequency using Woodwork’s “infer_frequency” function and generate a new DataFrame that follows it. In order to maintain as much original information from the input data as possible, all rows with completely correct times are
ported over into this new DataFrame. If there are any rows that have the same timestamp as another, these will be dropped. The first occurrence of a date or time maintains priority. If there are any values that don’t quite line up with the inferred frequency they will be shifted to any closely missing datetimes, or dropped if there are none nearby.
[9]:
from evalml.pipelines.components import TimeSeriesRegularizer
regularizer = TimeSeriesRegularizer(time_index="Date")
X_train, y_train = regularizer.fit_transform(X_train, y_train)
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
Now we can see that pandas has successfully inferred the frequency of the training data, and there are no more null values within X_train
. However, by adding values that were dropped before, we have created NaN values within the target data, as well as the other columns in our training data.
[10]:
print(f"Inferred frequency: {pd.infer_freq(X_train['Date'])}")
print(f"NaNs in training data: {X_train['Date'].isna().any()}")
print(
f"NaNs in other training data columns: {X_train['Categorical'].isna().any() or X_train['Numeric'].isna().any()}"
)
print(f"NaNs in target data: {y_train.isna().any()}")
Inferred frequency: D
NaNs in training data: False
NaNs in other training data columns: True
NaNs in target data: True
Time Series Imputer#
We could easily use the Imputer
and TargetImputer
components to fill in the missing gaps in our X
and y
data. However, these tools are not built for time series problems. Their supported imputation strategies of “mean”, “most_frequent”, or similar are all static. They don’t account for the passing of time, and how neighboring data points may have more predictive power than simply taking the average. The TimeSeriesImputer
solves this problem by offering three different
imputation strategies: - “forwards_fill”: fills in any NaN values with the same value as found in the previous non-NaN cell. - “backwards_fill”: fills in any NaN values with the same value as found in the next non-NaN cell. - “interpolate”: (numeric columns only) fills in any NaN values by linearly interpolating the values of the previous and next non-NaN cells.
[11]:
from evalml.pipelines.components import TimeSeriesImputer
ts_imputer = TimeSeriesImputer(
categorical_impute_strategy="forwards_fill",
numeric_impute_strategy="backwards_fill",
target_impute_strategy="interpolate",
)
X_train, y_train = ts_imputer.fit_transform(X_train, y_train)
Now, finally, we have a DataFrame that’s back in order without flaws, which we can use for running AutoMLSearch and running models without issue.
[12]:
print(f"Inferred frequency: {pd.infer_freq(X_train['Date'])}")
print(f"NaNs in training data: {X_train['Date'].isna().any()}")
print(
f"NaNs in other training data columns: {X_train['Categorical'].isna().any() or X_train['Numeric'].isna().any()}"
)
print(f"NaNs in target data: {y_train.isna().any()}")
Inferred frequency: D
NaNs in training data: False
NaNs in other training data columns: False
NaNs in target data: False
Trending and Seasonality Decomposition#
Decomposing a target signal into a trend and/or a cyclical signal is a common pre-processing step for time series modeling. Having an understanding of the presence or absence of these component signals can provide additional insight and decomposing the signal into these constituent components can enable non-time-series aware estimators to perform better while attempting to model this data. We have two unique decomposers, the PolynomialDecompser
and the STLDecomposer
.
Let’s first take a look at a year’s worth of the weather dataset.
[13]:
import matplotlib.pyplot as plt
length = 365
X_train_time = X_train.set_index("Date").asfreq(pd.infer_freq(X_train["Date"]))
y_train_time = y_train.set_axis(X_train["Date"]).asfreq(pd.infer_freq(X_train["Date"]))
plt.plot(y_train_time[0:length], "bo")
plt.show()
With the knowledge that this is a weather dataset and the data itself is daily weather data, we can assume that the seasonal data will have a period of approximately 365 data points. Let’s build and fit decomposers to detrend and deseasonalize this data.
Polynomial Decomposer#
[14]:
from evalml.pipelines.components.transformers.preprocessing.polynomial_decomposer import (
PolynomialDecomposer,
)
pdc = PolynomialDecomposer(degree=1, period=365)
X_t, y_t = pdc.fit_transform(X_train_time, y_train_time)
plt.plot(y_train_time, "bo", label="Signal")
plt.plot(y_t, "rx", label="Detrended/Deseasonalized Signal")
plt.legend()
plt.show()
The result is the residual signal, with the trend and seasonality removed. If we want to look at what the component identified as the trend and seasonality, we can call the plot_decomposition()
function.
[15]:
res = pdc.plot_decomposition(X_train_time, y_train_time)
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
It is desirable to enhance the decomposer component with a guess at the period of the seasonal aspect of the signal before decomposing it. To do that, we can use the determine_periodicity(X, y)
function of the Decomposer
class.
[16]:
period = pdc.determine_periodicity(X_train_time, y_train_time)
print(period)
351
The PolynomialDecomposer
class, if not explicitly set in the constructor, will set its period
parameter based on a statsmodels
function freq_to_period
that considers the frequency of the datetime data. This will give a reasonable guess as to what the frequency could be. For example, if the PolynomialDecomposer
object is fit with period
not explicitly set, it will take on a default value of 7, which is good for daily data signals that have a known seasonal component period
that is weekly.
In this case where the seasonal period is not known beforehand, the set_period()
convenience function will look at the target data, determine a better guess for the period and set the Decomposer
object appropriately.
[17]:
pdc = PolynomialDecomposer()
pdc.fit(X_train_time, y_train_time)
assert pdc.period == 7
pdc.set_period(X_train_time, y_train_time)
assert 350 < pdc.period < 370
STLDecomposer#
The STLDecomposer
runs on statsmodels’ implementation of STL decomposition. Let’s take a look at how STL decomposes the weather dataset.
[18]:
from evalml.pipelines.components import STLDecomposer
stl = STLDecomposer()
X_t, y_t = stl.fit_transform(X_train_time, y_train_time)
res = stl.plot_decomposition(X_train_time, y_train_time)
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
This doesn’t look nearly as good as the PolynomialDecomposer
did. This is because STL decomposition performs best when the data has a small seasonal period, generally less than 14 time units. The weather dataset’s seasonal period of ~365 days does not work as well since STL extracted a shorter term seasonality for decomposition.
We can generate some synthetic data that better highlights where STL performs well. For this example, we’ll generate monthly data with an annual seasonal period.
[19]:
import random
import numpy as np
from datetime import datetime
from sklearn.preprocessing import minmax_scale
def generate_synthetic_data(
period=12,
num_periods=25,
scale=10,
seasonal_scale=2,
trend_degree=1,
freq_str="M",
):
freq = 2 * np.pi / period
x = np.arange(0, period * num_periods, 1)
dts = pd.date_range(datetime.today(), periods=len(x), freq=freq_str)
X = pd.DataFrame({"x": x})
X = X.set_index(dts)
if trend_degree == 1:
y_trend = pd.Series(scale * minmax_scale(x + 2))
elif trend_degree == 2:
y_trend = pd.Series(scale * minmax_scale(x**2))
elif trend_degree == 3:
y_trend = pd.Series(scale * minmax_scale((x - 5) ** 3 + x**2))
y_seasonal = pd.Series(seasonal_scale * np.sin(freq * x))
y_random = pd.Series(np.random.normal(0, 1, len(X)))
y = y_trend + y_seasonal + y_random
return X, y
X_stl, y_stl = generate_synthetic_data()
Let’s see how the STLDecomposer
does at decomposing this data.
[20]:
stl = STLDecomposer()
X_t_stl, y_t_stl = stl.fit_transform(X_stl, y_stl)
res = stl.plot_decomposition(X_stl, y_stl)
On top of decomposing this type of data well, the statsmodels implementation of STL automatically determines the seasonal period of the data, which is saved during fit time for this component.
[21]:
stl = STLDecomposer()
assert stl.period is None
stl.fit(X_stl, y_stl)
print(stl.period)
None
Running AutoMLSearch#
AutoMLSearch
for time series problems works very similarly to the other problem types with the exception that users need to pass in a new parameter called problem_configuration
.
The problem_configuration
is a dictionary specifying the following values:
forecast_horizon: The number of time periods we are trying to forecast. In this example, we’re interested in predicting weather for the next 7 days, so the value is 7.
gap: The number of time periods between the end of the training set and the start of the test set. For example, in our case we are interested in predicting the weather for the next 7 days with the data as it is “today”, so the gap is 0. However, if we had to predict the weather for next Monday-Sunday with the data as it was on the previous Friday, the gap would be 2 (Saturday and Sunday separate Monday from Friday). It is important to select a value that matches the realistic delay between the forecast date and the most recently avaliable data that can be used to make that forecast.
max_delay: The maximum number of rows to look in the past from the current row in order to compute features. In our example, we’ll say we can use the previous week’s weather to predict the current week’s.
time_index: The column of the training dataset that contains the date corresponding to each observation. While only some of the models we run during time series searches require the
time_index
, we require it to be passed in to top level search so that the parameter can reach the models that need it.
Note that the values of these parameters must be in the same units as the training/testing data.
Visualization of forecast horizon and gap#
[22]:
from evalml.automl import AutoMLSearch
problem_config = {"gap": 0, "max_delay": 7, "forecast_horizon": 7, "time_index": "Date"}
automl = AutoMLSearch(
X_train,
y_train,
problem_type="time series regression",
max_batches=1,
problem_configuration=problem_config,
automl_algorithm="iterative",
allowed_model_families=[
"xgboost",
"random_forest",
"linear_model",
"extra_trees",
],
)
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/evalml/automl/automl_search.py:545: UserWarning:
Time series support in evalml is still in beta, which means we are still actively building its core features. Please be mindful of that when running search().
[23]:
automl.search()
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
[23]:
{1: {'Elastic Net Regressor w/ Imputer + Time Series Featurizer + STL Decomposer + DateTime Featurizer + One Hot Encoder + Drop NaN Rows Transformer + Standard Scaler': 14.467719793319702,
'Elastic Net Regressor w/ Imputer + Time Series Featurizer + DateTime Featurizer + One Hot Encoder + Drop NaN Rows Transformer + Standard Scaler': 3.0715291500091553,
'XGBoost Regressor w/ Imputer + Time Series Featurizer + STL Decomposer + DateTime Featurizer + One Hot Encoder': 14.905313968658447,
'XGBoost Regressor w/ Imputer + Time Series Featurizer + DateTime Featurizer + One Hot Encoder': 3.6762142181396484,
'Random Forest Regressor w/ Imputer + Time Series Featurizer + STL Decomposer + DateTime Featurizer + One Hot Encoder + Drop NaN Rows Transformer': 17.18693208694458,
'Random Forest Regressor w/ Imputer + Time Series Featurizer + DateTime Featurizer + One Hot Encoder + Drop NaN Rows Transformer': 5.750487327575684,
'Extra Trees Regressor w/ Imputer + Time Series Featurizer + STL Decomposer + DateTime Featurizer + One Hot Encoder + Drop NaN Rows Transformer': 14.493677616119385,
'Extra Trees Regressor w/ Imputer + Time Series Featurizer + DateTime Featurizer + One Hot Encoder + Drop NaN Rows Transformer': 3.102419853210449,
'Total time of batch': 77.46657371520996}}
Understanding what happened under the hood#
This is great, AutoMLSearch
is able to find a pipeline that scores an R2 value of 0.44 compared to a baseline pipeline that is only able to score 0.07. But how did it do that?
Data Splitting#
EvalML uses rolling origin cross validation for time series problems. Basically, we take successive cuts of the training data while keeping the validation set size fixed at forecast_horizon
number of time units. Note that the splits are not separated by gap
number of units. This is because we need access to all the data to generate features for every row of the validation set. However, the feature engineering done by our pipelines respects
the gap
value. This is explained more in the feature engineering section.
Baseline Pipeline#
The most naive thing we can do in a time series problem is use the most recently available observation to predict the next observation. In our example, this means we’ll use the measurement from 7
days ago as the prediction for the current date.
[24]:
import pandas as pd
baseline = automl.get_pipeline(0)
baseline.fit(X_train, y_train)
naive_baseline_preds = baseline.predict_in_sample(
X_test, y_test, objective=None, X_train=X_train, y_train=y_train
)
expected_preds = pd.Series(
pd.concat([y_train.iloc[-7:], y_test]).shift(7).iloc[7:], name="target"
)
pd.testing.assert_series_equal(expected_preds, naive_baseline_preds)
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
Feature Engineering#
EvalML uses the values of gap
, forecast_horizon
, and max_delay
to calculate a “window” of allowed dates that can be used for engineering the features of each row in the validation/test set. The formula for computing the bounds of the window is:
[t - (max_delay + forecast_horizon + gap), t - (forecast_horizon + gap)]
As an example, this is what the features for the first five days of August would look like in our current problem:
The estimator then takes these features to generate predictions:
Feature engineering components for time series#
For an example of a time-series feature engineering component see TimeSeriesFeaturizer
Evaluate best pipeline on test data#
Now that we have covered the mechanics of how EvalML runs AutoMLSearch for time series pipelines, we can compare the performance on the test set of the best pipeline found during search and the baseline pipeline.
[25]:
pl = automl.best_pipeline
pl.fit(X_train, y_train)
best_pipeline_score = pl.score(X_test, y_test, ["MedianAE"], X_train, y_train)[
"MedianAE"
]
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
[26]:
best_pipeline_score
[26]:
1.8867196350647752
[27]:
baseline = automl.get_pipeline(0)
baseline.fit(X_train, y_train)
naive_baseline_score = baseline.score(X_test, y_test, ["MedianAE"], X_train, y_train)[
"MedianAE"
]
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
[28]:
naive_baseline_score
[28]:
2.3
The pipeline found by AutoMLSearch has a 31% improvement over the naive forecast!
[29]:
automl.objective.calculate_percent_difference(best_pipeline_score, naive_baseline_score)
[29]:
17.96871151892281
Visualize the predictions over time#
[30]:
from evalml.model_understanding import graph_prediction_vs_actual_over_time
fig = graph_prediction_vs_actual_over_time(
pl, X_test, y_test, X_train, y_train, dates=X_test["Date"]
)
fig
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
Predicting on unseen data#
You’ll notice that in the code snippets here, we use the predict_in_sample
pipeline method as opposed to the usual predict
method. What’s the difference?
predict_in_sample
is used when the target value is known on the dates we are predicting on. This is true in cross validation. This method has an expectedy
parameter so that we can compute features using previous target values for all of the observations on the holdout set.predict
is used when the target value is not known, e.g. the test dataset. The y parameter is not expected as only the target is observed in the training set. The test dataset must be separated bygap
units from the training dataset. For the moment, the test set size must be less than or equal toforecast_horizon
.
Here is an example of these two methods in action:
predict_in_sample#
[31]:
pl.predict_in_sample(X_test, y_test, objective=None, X_train=X_train, y_train=y_train)
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
[31]:
3287 13.124784
3288 13.468004
3289 13.472009
3290 12.724985
3291 11.868053
...
3647 14.159499
3648 13.257666
3649 13.365598
3650 13.731870
3651 12.263210
Name: target, Length: 365, dtype: float64
predict#
[32]:
pl.predict(X_test, objective=None, X_train=X_train, y_train=y_train)
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
[32]:
3287 13.124784
3288 13.468004
3289 13.472009
3290 12.724985
3291 11.868053
...
3647 18.339399
3648 18.393293
3649 18.464086
3650 18.354281
3651 18.330683
Name: target, Length: 365, dtype: float64
Validating the holdout data#
Before we predict on our holdout data, it is important to validate that it meets the requirements we summarized in the second point above in Predicting on unseen data. We can call on validate_holdout_datasets
in order to verify the two requirements:
The holdout data is separated by
gap
units from the training dataset. This is determined by thetime_index
column, not the index e.g. if your datetime frequency for the column “Date” is 2 days with agap
of 3, then the holdout data must start 2 days x 3 = 6 days after the training data.The length of the holdout data must be less than or equal to the
forecast_horizon
.
[33]:
from evalml.utils.gen_utils import validate_holdout_datasets
# Holdout dataset has 365 observations
validation_results = validate_holdout_datasets(X_test, X_train, problem_config)
assert not validation_results.is_valid
# Holdout dataset has 7 observations
validation_results = validate_holdout_datasets(
X_test.iloc[: pl.forecast_horizon], X_train, problem_config
)
assert validation_results.is_valid
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
predict – Test set size matches forecast horizon#
[34]:
pl.predict(
X_test.iloc[: pl.forecast_horizon], objective=None, X_train=X_train, y_train=y_train
)
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
[34]:
3287 13.124784
3288 13.468004
3289 13.472009
3290 12.724985
3291 11.868053
3292 13.315326
3293 12.943245
Name: target, dtype: float64
predict – Test set size is less than forecast horizon#
[35]:
pl.predict(
X_test.iloc[: pl.forecast_horizon - 2],
objective=None,
X_train=X_train,
y_train=y_train,
)
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
[35]:
3287 13.124784
3288 13.468004
3289 13.472009
3290 12.724985
3291 11.868053
Name: target, dtype: float64
predict – Test set size index starts at 0#
[36]:
pl.predict(
X_test.iloc[: pl.forecast_horizon].reset_index(drop=True),
objective=None,
X_train=X_train,
y_train=y_train,
)
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
[36]:
3287 13.124784
3288 13.468004
3289 13.472009
3290 12.724985
3291 11.868053
3292 13.315326
3293 12.943245
Name: target, dtype: float64
Prediction Intervals#
Getting Prediction Intervals#
While predictions that are generated by EvalML pipelines aim to be accurate as possible, it is very rarely the case that future results are the exact same values as predicted. Prediction intervals can help to contextualize a prediction by showing the range a future prediction is expected to fall within a certain likelihood.
Given the preprocessed (transformed, ready for prediction) features, the corresponding predictions, and a fitted EvalML estimator, the prediction intervals for this set of predictions is generated by calling get_prediction_intervals()
on the pipeline’s estimator. Here, we use the fitted estimator in our trained EvalML pipeline to generate the prediction intervals:
[37]:
X_trans = pl.transform_all_but_final(X_test, y_test)
y_pred = pl.predict(X_test, objective=None, X_train=X_train, y_train=y_train)
pl.estimator.get_prediction_intervals(X=X_trans, y=y_pred)
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
[37]:
{'0.95_lower': 3299 13.053448
3300 13.059237
3301 13.561511
3302 13.934604
3303 14.108101
...
3647 12.722115
3648 12.465191
3649 12.651836
3650 13.000082
3651 10.880485
Length: 353, dtype: float64,
'0.95_upper': 3299 14.960487
3300 14.966275
3301 15.468550
3302 15.841642
3303 16.015140
...
3647 15.596882
3648 14.050141
3649 14.079360
3650 14.463658
3651 13.645936
Length: 353, dtype: float64}
By default, prediction intervals are calculated for the 95% upper and lower bound. In the above example, 95% of the time, a prediction sometime in the future will fall in this range.
To generate prediction intervals for a custom value, use the coverage
parameter. In the example below, the 80% interval range is calculated below:
[38]:
pl.estimator.get_prediction_intervals(X=X_trans, y=y_pred, coverage=[0.8])
[38]:
{'0.8_lower': 3299 13.383495
3300 13.389283
3301 13.891558
3302 14.264650
3303 14.438148
...
3647 13.219644
3648 12.739495
3649 12.898894
3650 13.253380
3651 11.359095
Length: 353, dtype: float64,
'0.8_upper': 3299 14.630440
3300 14.636229
3301 15.138503
3302 15.511596
3303 15.685093
...
3647 15.099353
3648 13.775838
3649 13.832302
3650 14.210360
3651 13.167326
Length: 353, dtype: float64}
Forecasting Future Data#
Unlike standard pipelines, time series pipelines are able to generate predictions out to the future. The number of predictions out in the future is dependent on the forecast_horizon
parameter set in the problem configuration of an AutoML search.
To show that it is possible to generate brand new predictions in the future, the entire weather dataset (including the holdout set) will be used. The code block below refit the pipeline on the entire dataset and generates a forecast.
[39]:
X.ww.init()
y.ww.init()
pl.fit(X, y)
X_forecast_dates = pl.get_forecast_period(X=X).to_frame()
y_forecast = pl.get_forecast_predictions(X=X, y=y)
display("Forecast Dates:", X_forecast_dates)
display("Forecast Predictions:", y_forecast)
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
'Forecast Dates:'
Date | |
---|---|
3652 | 1991-01-01 |
3653 | 1991-01-02 |
3654 | 1991-01-03 |
3655 | 1991-01-04 |
3656 | 1991-01-05 |
3657 | 1991-01-06 |
3658 | 1991-01-07 |
'Forecast Predictions:'
3652 13.131977
3653 12.556440
3654 13.066180
3655 13.394714
3656 12.375170
3657 13.541838
3658 13.348718
Name: Temp, dtype: float64
Using these forecasted values, it is possible to generate the prediction intervals for each forecasted point.
[40]:
res = pl.get_prediction_intervals(
X=pd.DataFrame(X_forecast_dates), y=y_forecast, X_train=X, y_train=y
)
display(res)
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/woodwork/type_sys/utils.py:33: UserWarning:
Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.
{'0.95_lower': 3652 7.140981
3653 4.083893
3654 2.689471
3655 1.412723
3656 -1.021104
3657 -1.133045
3658 -2.501967
Name: 0.95_lower, dtype: float64,
'0.95_upper': 3652 19.122973
3653 21.028988
3654 23.442889
3655 25.376706
3656 25.771444
3657 28.216721
3658 29.199403
Name: 0.95_upper, dtype: float64}
[41]:
y_lower = res["0.95_lower"]
y_upper = res["0.95_upper"]
Using the forecasted predictions and corresponding prediction intervals, we can plot this data. For this plot, only the last 31 days of data will be used so that the forecasted data is visible.
[42]:
X_before = X[-31:]
y_before = y[-31:]
[43]:
fig = go.Figure(
[
# Plot last 31 days of training data
go.Scatter(x=X_before["Date"], y=y_before, name="Training Data", mode="lines"),
# Plot forecast data
go.Scatter(
x=X_forecast_dates["Date"], y=y_forecast, name="Forecast Data", mode="lines"
),
# Plot prediction intervals
go.Scatter(
x=pd.concat([X_forecast_dates["Date"], X_forecast_dates["Date"][::-1]]),
y=pd.concat([y_upper, y_lower[::-1]]),
fill="toself",
fillcolor="rgba(255,0,0,0.2)",
line=dict(color="rgba(255,0,0,0.2)"),
name="Forecast Prediction Intervals",
showlegend=True,
),
],
layout={
"title": "Plot of Last Two Weeks of Data + Forecast Data With Prediction Intervals",
"xaxis": dict(title="Date"),
"yaxis": dict(title="Temperature (C)"),
},
)
fig.show()
Forecasting into the future#
Our previous examples have shown using a pipeline to predict on data we had at training time. However, we can also use EvalML time series pipelines to forecast dates into the future as long we provide data that meets the requirements of Predicting on unseen data as well.
To help figure out the dates we need in X_train
to forecast dates into the future - we’ve provided dates_needed_for_prediction
and dates_needed_for_prediction_range
.
[44]:
forecast_date = pd.Timestamp("1991-01-07")
beginning_date, end_date = pl.dates_needed_for_prediction(forecast_date)
print("Dates needed:")
print(f"{beginning_date.strftime('%Y-%m-%d %X')} to {end_date.strftime('%Y-%m-%d %X')}")
Dates needed:
1990-12-23 00:00:00 to 1991-01-06 00:00:00
We can see how the dates are valid by generating some future dates and features with the above date range.
[45]:
import random
dates = pd.date_range(
beginning_date,
end_date,
freq=pl.frequency.split("-")[0],
)
X_train_forecast = pd.DataFrame(index=[i + 1 for i in range(len(dates))])
categorical_feature = pd.Series(
[random.randint(0, 3) for i in range(len(dates))], index=X_train_forecast.index
)
numeric_feature = pd.Series(
[i + 1 for i in range(len(dates))], index=X_train_forecast.index
)
X_train_forecast["Date"] = pd.Series(dates.values, index=X_train_forecast.index)
X_train_forecast["Categorical"] = pd.Series(
categorical_feature.values, index=X_train_forecast.index
)
X_train_forecast["Numeric"] = pd.Series(
numeric_feature.values, index=X_train_forecast.index
)
X_train_forecast.ww.init(
logical_types={"Categorical": "categorical", "Numeric": "integer"}
)
y_train_forecast = pd.Series(
X_train_forecast["Numeric"].values, index=X_train_forecast.index
)
[46]:
X_test_forecast = pd.DataFrame(
{"Date": [forecast_date], "Categorical": [3], "Numeric": [53862]}
)
… and we succesfully have our prediction!
[47]:
pl.predict(X_test_forecast, X_train=X_train_forecast, y_train=y_train_forecast)
[47]:
16 8.930661
Name: Temp, dtype: float64
[48]:
forecast_start = pd.Timestamp("1991-01-07")
forecast_end = pd.Timestamp("1991-01-14")
dates = pl.dates_needed_for_prediction_range(forecast_start, forecast_end)
print("Dates needed:")
print(f"{dates[0].strftime('%Y-%m-%d %X')} to {dates[1].strftime('%Y-%m-%d %X')}")
Dates needed:
1990-12-23 00:00:00 to 1991-01-13 00:00:00
Known-in-advance features#
In time series problems, the goal is to predict an unknown value of a data series corresponding to a future moment in time. Since the state of the world is not known in the future, we create features from data in the past since those values are known when we go make our prediction.
However, there are some features corresponding to dates in the future that can be known with certainty, either because they can be derived from the forecast date or because the feature can be controlled by the modeler. This includes features such as if the date is a US Holiday, or the location of a store in a sales dataset. With these sorts of features, we don’t need to include them in our time-series specific preprocessing steps (such as Time Series Featurization).
To handle these features, EvalML will split them into a separate path through the component graph, bypassing the unnecessary preprocessing steps. Let’s take a look at what that looks like, using some synthetic data.
[50]:
X = pd.DataFrame(
{"features": range(101, 601), "date": pd.date_range("2010-10-01", periods=500)}
)
y = pd.Series(range(500))
X.ww.init()
X.ww["bool_feature"] = (
pd.Series([True, False]).sample(n=X.shape[0], replace=True).reset_index(drop=True)
)
X.ww["cat_feature"] = (
pd.Series(["a", "b", "c"]).sample(n=X.shape[0], replace=True).reset_index(drop=True)
)
automl = AutoMLSearch(
X,
y,
problem_type="time series regression",
problem_configuration={
"max_delay": 5,
"gap": 3,
"forecast_horizon": 2,
"time_index": "date",
"known_in_advance": ["bool_feature", "cat_feature"],
},
)
automl.search()
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/statsmodels/tsa/stattools.py:702: RuntimeWarning:
invalid value encountered in divide
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/statsmodels/tsa/stattools.py:702: RuntimeWarning:
invalid value encountered in divide
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/statsmodels/tsa/stattools.py:702: RuntimeWarning:
invalid value encountered in divide
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/statsmodels/tsa/stattools.py:702: RuntimeWarning:
invalid value encountered in divide
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/statsmodels/tsa/stattools.py:702: RuntimeWarning:
invalid value encountered in divide
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/statsmodels/tsa/stattools.py:702: RuntimeWarning:
invalid value encountered in divide
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/statsmodels/tsa/stattools.py:702: RuntimeWarning:
invalid value encountered in divide
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/statsmodels/tsa/stattools.py:702: RuntimeWarning:
invalid value encountered in divide
19:56:45 - cmdstanpy - INFO - Chain [1] start processing
19:56:45 - cmdstanpy - INFO - Chain [1] done processing
19:56:46 - cmdstanpy - INFO - Chain [1] start processing
19:56:46 - cmdstanpy - INFO - Chain [1] done processing
19:56:46 - cmdstanpy - INFO - Chain [1] start processing
19:56:46 - cmdstanpy - INFO - Chain [1] done processing
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000182 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 1997
[LightGBM] [Info] Number of data points in the train set: 494, number of used features: 19
[LightGBM] [Info] Start training from score 246.500000
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000147 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2006
[LightGBM] [Info] Number of data points in the train set: 496, number of used features: 19
[LightGBM] [Info] Start training from score 247.500000
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000163 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 2015
[LightGBM] [Info] Number of data points in the train set: 498, number of used features: 19
[LightGBM] [Info] Start training from score 248.500000
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[50]:
{1: {'Random Forest Regressor w/ Select Columns Transformer + Imputer + Time Series Featurizer + DateTime Featurizer + Select Columns Transformer + Imputer + One Hot Encoder + Drop NaN Rows Transformer': 1.8715941905975342,
'Total time of batch': 2.0195670127868652},
2: {'ARIMA Regressor w/ Select Columns Transformer + Imputer + Time Series Featurizer + Select Columns Transformer + Imputer + One Hot Encoder': 41.52375602722168,
'Exponential Smoothing Regressor w/ Select Columns Transformer + Imputer + Time Series Featurizer + DateTime Featurizer + Select Columns Transformer + Imputer + One Hot Encoder': 0.9804470539093018,
'Prophet Regressor w/ Select Columns Transformer + Imputer + Time Series Featurizer + Select Columns Transformer + Imputer + One Hot Encoder': 1.3640289306640625,
'Extra Trees Regressor w/ Select Columns Transformer + Imputer + Time Series Featurizer + DateTime Featurizer + Select Columns Transformer + Imputer + One Hot Encoder + Drop NaN Rows Transformer': 1.376312017440796,
'XGBoost Regressor w/ Select Columns Transformer + Imputer + Time Series Featurizer + DateTime Featurizer + Select Columns Transformer + Imputer + One Hot Encoder': 1.5462892055511475,
'LightGBM Regressor w/ Select Columns Transformer + Imputer + Time Series Featurizer + DateTime Featurizer + Select Columns Transformer + Imputer + One Hot Encoder': 1.204615831375122,
'Elastic Net Regressor w/ Select Columns Transformer + Imputer + Time Series Featurizer + DateTime Featurizer + Standard Scaler + Select Columns Transformer + Imputer + One Hot Encoder + Standard Scaler + Drop NaN Rows Transformer': 1.3633010387420654,
'Total time of batch': 50.3933002948761}}
[51]:
pipeline = automl.best_pipeline
pipeline.graph()
[51]:
Multiseries time series problems#
The above documentation has focused on single series time series data. Now, we take a look at multiseries time series forecasting.
What is multiseries?#
Multiseries time series refers to data where we have multiple time series that we’re trying to forecast simultaneously. For example, if we are a retailer who sells multiple products at our stores, we may have a single dataset that contains sales data for those multiple products. In this case, we would like to forecast sales for all products without splitting them off into separate datasets.
There are two forms of multiseries forecasting - independent and dependent. Independent forecasting assumes that the separate series we’re modeling are independent from each other, that is, the value of one series at a given point in time is unrelated to the value of a different series at any point in time. In our sales example, product A sales and product B sales would not impact each other at all. Dependent forecasting is the opposite, where it is assumed that all series have an impact on the others in the dataset.
At the moment, EvalML only supports independent multiseries time series forecasting.
Preparing the data#
For this example, we will generate a fake example dataset with just two example series. It is common that many real-world scenarios will have more.
[52]:
import numpy as np
time_index = list(pd.date_range(start="1/1/2018", periods=50)) * 2
series_id = pd.Series([1] * 50 + [2] * 50, dtype="str")
series_1_target = pd.Series(10 * np.sin(np.arange(50)) + np.random.rand(50))
series_2_target = pd.Series(range(50) + np.random.rand(50))
target = pd.Series(
pd.concat([series_1_target, series_2_target]), name="target"
).reset_index(drop=True)
data = (
pd.DataFrame(
{
"date": time_index,
"series_id": series_id,
"target": target,
},
)
.sort_values("date")
.reset_index(drop=True)
)
X = data.drop(["target"], axis=1)
y = data["target"]
This data has two unique series with their own target patterns, combined into a single dataset. Notice that both targets are combined into a single column, with each series’ targets being delineated by the series_id
. These values could be in essentially any order, as long as the date, series id, and target values are in equivalent rows. Note that the series id column can be categorical, it does not need to be numeric as it is in this example. Also note that while this example does not
contain any other non-target features, the VARMAXRegressor
can handle them.
[53]:
data.head(6)
[53]:
date | series_id | target | |
---|---|---|---|
0 | 2018-01-01 | 1 | 0.166590 |
1 | 2018-01-01 | 2 | 0.221638 |
2 | 2018-01-02 | 1 | 9.062568 |
3 | 2018-01-02 | 2 | 1.562194 |
4 | 2018-01-03 | 1 | 10.077649 |
5 | 2018-01-03 | 2 | 2.444950 |
In EvalML, we refer to this general data format as being “stacked” data, where all the target values for the separate series are stacked into a single column. If we removed the series id column and broke them into separate columns with one for each series, that would be referred to as “unstacked” data. EvalML provides utility functions to go between these formats, unstack_multiseries
, stack_data
, and stack_X
. The stacking functions are differentiated by their return type, where
stack_data
will stack the input into a single column while stack_X
will stack multiple series into their respective columns within a DataFrame.
[54]:
from evalml.pipelines.utils import unstack_multiseries
X_unstacked, y_unstacked = unstack_multiseries(
X, y, series_id="series_id", time_index="date", target_name="target"
)
y_unstacked.head(10)
[54]:
target|1 | target|2 | |
---|---|---|
0 | 0.166590 | 0.221638 |
1 | 9.062568 | 1.562194 |
2 | 10.077649 | 2.444950 |
3 | 1.521197 | 3.100179 |
4 | -6.798676 | 4.998101 |
5 | -9.220424 | 5.880111 |
6 | -2.024535 | 6.858512 |
7 | 6.624891 | 7.305845 |
8 | 10.407821 | 8.156308 |
9 | 4.421158 | 9.517341 |
We provide a separate utility function to split your data into training and holdout sets for multiseries data.
[55]:
from evalml.preprocessing import split_multiseries_data
X_train, X_holdout, y_train, y_holdout = split_multiseries_data(
X, y, series_id="series_id", time_index="date"
)
Running AutoMLSearch#
Running AutoMLSearch for multiseries time series forecasting is similar to any other single series time series problem. The data should be in its “stacked” format, and the problem configuration requires an additional parameter of series_id
.
Right now, the only estimator that supports multiseries time series regression forecasting outside of the baseline model is the VARMAXRegressor
(more information can be found here). The VARMAX regressor is trained during the first batch twice - once on its own and once running STL decomposition beforehand, as with single series instances.
[56]:
problem_configuration = {
"time_index": "date",
"forecast_horizon": 5,
"max_delay": 10,
"gap": 0,
"series_id": "series_id",
}
automl = AutoMLSearch(
X_train,
y_train,
problem_type="multiseries time series regression",
problem_configuration=problem_configuration,
)
automl.search()
/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/scipy/linalg/_solvers.py:215: LinAlgWarning:
Ill-conditioned matrix (rcond=5.41085e-50): result may not be accurate.
VARMAX Regressor w/ Imputer + Time Series Featurizer + STL Decomposer fold 1: Encountered an error.
VARMAX Regressor w/ Imputer + Time Series Featurizer + STL Decomposer fold 1: All scores will be replaced with nan.
Fold 1: Exception during automl search: LU decomposition error.
Fold 1: Parameters:
{'Imputer': {'categorical_impute_strategy': 'most_frequent', 'numeric_impute_strategy': 'mean', 'boolean_impute_strategy': 'most_frequent', 'categorical_fill_value': None, 'numeric_fill_value': None, 'boolean_fill_value': None}, 'Time Series Featurizer': {'time_index': 'date', 'max_delay': 10, 'delay_target': True, 'delay_features': True, 'forecast_horizon': 5, 'conf_level': 0.05, 'gap': 0, 'rolling_window_size': 0.25}, 'STL Decomposer': {'degree': 1, 'period': None, 'seasonal_smoother': 7, 'time_index': 'date', 'periods': None, 'series_id': 'series_id'}, 'VARMAX Regressor': {'order': (1, 0), 'trend': 'c', 'maxiter': 10, 'use_covariates': False, 'time_index': 'date', 'p': 1, 'q': 0}, 'pipeline': {'time_index': 'date', 'forecast_horizon': 5, 'max_delay': 10, 'gap': 0, 'series_id': 'series_id'}}
Fold 1: Traceback:
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/evalml/automl/engine/engine_base.py", line 227, in _train_and_score
fitted_pipeline, hashes = train_pipeline(
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/evalml/automl/engine/engine_base.py", line 165, in train_pipeline
cv_pipeline.fit(X, y)
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/evalml/utils/base_meta.py", line 26, in _set_fit
return_value = method(self, X, y)
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/evalml/pipelines/multiseries_regression_pipeline.py", line 69, in fit
self._fit(X, y)
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/evalml/pipelines/multiseries_regression_pipeline.py", line 85, in _fit
self.component_graph.fit(X_unstacked, y_unstacked)
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/evalml/pipelines/component_graph.py", line 257, in fit
self._transform_features(self.compute_order, X, y, fit=True)
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/evalml/pipelines/component_graph.py", line 532, in _transform_features
component_instance.fit(x_inputs, y_input)
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/evalml/utils/base_meta.py", line 26, in _set_fit
return_value = method(self, X, y)
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/evalml/pipelines/components/estimators/regressors/varmax_regressor.py", line 152, in fit
self._component_obj.fit(y=y)
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/sktime/forecasting/base/_base.py", line 391, in fit
self._fit(y=y_inner, X=X_inner, fh=fh)
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/sktime/forecasting/base/adapters/_statsmodels.py", line 60, in _fit
self._fit_forecaster(y, X)
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/sktime/forecasting/varmax.py", line 321, in _fit_forecaster
self._fitted_forecaster = self._forecaster.fit(
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/statsmodels/tsa/statespace/mlemodel.py", line 705, in fit
mlefit = super().fit(start_params, method=method,
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/statsmodels/base/model.py", line 566, in fit
xopt, retvals, optim_settings = optimizer._fit(f, score, start_params,
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/statsmodels/base/optimizer.py", line 243, in _fit
xopt, retvals = func(objective, gradient, start_params, fargs, kwargs,
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/statsmodels/base/optimizer.py", line 660, in _fit_lbfgs
retvals = optimize.fmin_l_bfgs_b(func, start_params, maxiter=maxiter,
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/scipy/optimize/_lbfgsb_py.py", line 237, in fmin_l_bfgs_b
res = _minimize_lbfgsb(fun, x0, args=args, jac=jac, bounds=bounds,
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/scipy/optimize/_lbfgsb_py.py", line 407, in _minimize_lbfgsb
f, g = func_and_grad(x)
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py", line 297, in fun_and_grad
self._update_grad()
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py", line 267, in _update_grad
self._update_grad_impl()
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py", line 181, in update_grad
self.g = approx_derivative(fun_wrapped, self.x, f0=self.f,
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/scipy/optimize/_numdiff.py", line 519, in approx_derivative
return _dense_difference(fun_wrapped, x0, f0, h,
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/scipy/optimize/_numdiff.py", line 590, in _dense_difference
df = fun(x) - f0
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/scipy/optimize/_numdiff.py", line 470, in fun_wrapped
f = np.atleast_1d(fun(x, *args, **kwargs))
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/scipy/optimize/_differentiable_functions.py", line 145, in fun_wrapped
fx = fun(np.copy(x), *args)
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/statsmodels/base/model.py", line 534, in f
return -self.loglike(params, *args) / nobs
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/statsmodels/tsa/statespace/mlemodel.py", line 940, in loglike
loglike = self.ssm.loglike(complex_step=complex_step, **kwargs)
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/statsmodels/tsa/statespace/kalman_filter.py", line 1001, in loglike
kfilter = self._filter(**kwargs)
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/statsmodels/tsa/statespace/kalman_filter.py", line 921, in _filter
self._initialize_state(prefix=prefix, complex_step=complex_step)
File "/home/docs/checkouts/readthedocs.org/user_builds/feature-labs-inc-evalml/envs/latest/lib/python3.9/site-packages/statsmodels/tsa/statespace/representation.py", line 1058, in _initialize_state
self._statespaces[prefix].initialize(self.initialization,
File "statsmodels/tsa/statespace/_representation.pyx", line 1362, in statsmodels.tsa.statespace._representation.dStatespace.initialize
File "statsmodels/tsa/statespace/_initialization.pyx", line 288, in statsmodels.tsa.statespace._initialization.dInitialization.initialize
File "statsmodels/tsa/statespace/_initialization.pyx", line 406, in statsmodels.tsa.statespace._initialization.dInitialization.initialize_stationary_stationary_cov
File "statsmodels/tsa/statespace/_tools.pyx", line 1548, in statsmodels.tsa.statespace._tools._dsolve_discrete_lyapunov
[56]:
{1: {'VARMAX Regressor w/ Imputer + Time Series Featurizer + STL Decomposer': 1.081374168395996,
'Extra Trees Regressor w/ Imputer + Time Series Featurizer + STL Decomposer + DateTime Featurizer + Drop NaN Rows Transformer': 2.6343793869018555,
'XGBoost Regressor w/ Imputer + Time Series Featurizer + STL Decomposer + DateTime Featurizer': 2.4201443195343018,
'Elastic Net Regressor w/ Imputer + Time Series Featurizer + STL Decomposer + DateTime Featurizer + Drop NaN Rows Transformer + Standard Scaler': 2.362603187561035,
'VARMAX Regressor w/ Imputer + Time Series Featurizer': 0.7177009582519531,
'Extra Trees Regressor w/ Imputer + Time Series Featurizer + DateTime Featurizer + Drop NaN Rows Transformer': 0.7516276836395264,
'XGBoost Regressor w/ Imputer + Time Series Featurizer + DateTime Featurizer': 0.5410842895507812,
'Elastic Net Regressor w/ Imputer + Time Series Featurizer + DateTime Featurizer + Drop NaN Rows Transformer + Standard Scaler': 0.4846653938293457,
'Total time of batch': 11.965147733688354}}
[57]:
automl.rankings
[57]:
id | pipeline_name | search_order | ranking_score | mean_cv_score | standard_deviation_cv_score | percent_better_than_baseline | high_variance_cv | parameters | |
---|---|---|---|---|---|---|---|---|---|
0 | 4 | Elastic Net Regressor w/ Imputer + Time Series... | 4 | 0.488228 | 0.488228 | 0.178114 | 90.632359 | False | {'Imputer': {'categorical_impute_strategy': 'm... |
1 | 2 | Extra Trees Regressor w/ Imputer + Time Series... | 2 | 0.491305 | 0.491305 | 0.120664 | 90.573310 | False | {'Imputer': {'categorical_impute_strategy': 'm... |
2 | 1 | VARMAX Regressor w/ Imputer + Time Series Feat... | 1 | 0.525540 | 0.525540 | 0.184801 | NaN | False | {'Imputer': {'categorical_impute_strategy': 'm... |
3 | 3 | XGBoost Regressor w/ Imputer + Time Series Fea... | 3 | 0.570501 | 0.570501 | 0.097637 | 89.053788 | False | {'Imputer': {'categorical_impute_strategy': 'm... |
4 | 8 | Elastic Net Regressor w/ Imputer + Time Series... | 8 | 0.746683 | 0.746683 | 0.237223 | 85.673371 | False | {'Imputer': {'categorical_impute_strategy': 'm... |
5 | 7 | XGBoost Regressor w/ Imputer + Time Series Fea... | 7 | 3.566132 | 3.566132 | 1.361062 | 31.576526 | False | {'Imputer': {'categorical_impute_strategy': 'm... |
6 | 6 | Extra Trees Regressor w/ Imputer + Time Series... | 6 | 3.932490 | 3.932490 | 0.818803 | 24.547202 | False | {'Imputer': {'categorical_impute_strategy': 'm... |
7 | 0 | Multiseries Time Series Baseline Pipeline | 0 | 5.211854 | 5.211854 | 0.303734 | 0.000000 | False | {'Time Series Featurizer': {'time_index': 'dat... |
8 | 5 | VARMAX Regressor w/ Imputer + Time Series Feat... | 5 | 5.630218 | 5.630218 | 2.263266 | -8.027157 | False | {'Imputer': {'categorical_impute_strategy': 'm... |
Visualizing the data and results#
As with any other time series problem, EvalML provides utility functions to visualize the data.
Decomposition#
Plotting the decomposition of our multiseries data displays each series’ decomposition in order. The decomposition object, unlike AutoMLSearch, requires unstacked data.
[58]:
decomposer = STLDecomposer(time_index="date", series_id="series_id")
decomposer.fit(X_unstacked, y_unstacked)
decomposer.plot_decomposition(X_unstacked, y_unstacked)
[58]:
{'target|1': (<Figure size 1850x1450 with 4 Axes>,
array([<Axes: title={'center': 'signal'}>,
<Axes: title={'center': 'trend'}>,
<Axes: title={'center': 'seasonality'}>,
<Axes: title={'center': 'residual'}>], dtype=object)),
'target|2': (<Figure size 1850x1450 with 4 Axes>,
array([<Axes: title={'center': 'signal'}>,
<Axes: title={'center': 'trend'}>,
<Axes: title={'center': 'seasonality'}>,
<Axes: title={'center': 'residual'}>], dtype=object))}
Prediction vs actual#
Similarly, we can visualize the performance of our models by examining the prediction compared to the actual values over time. If we’d like to see the results of a single series, rather than all of them, we can use the argument single_series
.
[59]:
pipeline = automl.best_pipeline
fig = graph_prediction_vs_actual_over_time(
pipeline, X_holdout, y_holdout, X_train, y_train, dates=X_holdout["date"]
)
fig
[60]:
fig = graph_prediction_vs_actual_over_time(
pipeline,
X_holdout,
y_holdout,
X_train,
y_train,
dates=X_holdout["date"],
single_series="2",
)
fig