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Time series feature lists

DataRobot automatically constructs time series features based on the characteristics of the data (e.g., stationarity and periodicities). Multiple periodicities can result in several possibilities when it comes to constructing the features—both “Sales (7 day diff) (1st lag)” or “Sales (24 hour diff) (1st lag)” can make sense, for example. In some cases, it is better to perform no transforming of the target by differencing. The choice that yields the optimal accuracy often depends on the data.

After constructing time series features for the data, DataRobot creates multiple feature lists (the target is automatically included in each). Then, at project start, DataRobot automatically runs blueprints using multiple feature lists, selecting the list that best suits the model type. With non-time series projects, by contrast, blueprints run on a single feature list, typically Informative Features).

Time series feature lists can be viewed from the Data > Derived Modeling Data page, for example:

These lists are different, and more targeted, than those created by non-time series projects.

Exclude features from feature lists

There are times when you cannot exclude features from derivation because other features rely on those features. Instead, you can exclude them from a feature list. In that way, they are still used in initial feature derivation but are excluded from modeling.

Note the following behavior that results from excluding certain special features from feature lists:

  • Target column: DataRobot will not derive target-derived features.

  • Primary date/time column: DataRobot will not derive calendar and duration features.

  • Series ID column: DataRobot will not generate any models that depend on the series ID, including per-series, series-level effects, or hierarchical models.

MASE and baseline models

The baseline model is a model that uses the most recent value that matches the longest periodicity. That is, while a project could have multiple different näive predictions with different periodicity, DataRobot uses the longest näive predictions to compute the MASE score. MASE is a measure of the accuracy of forecasts, and is a comparison of one model to a näive baseline model—the simple ratio of the MAE of a model over the baseline model.

DataRobot identifies which model is being used as the baseline model for time series projects with a BASELINE badge.

To build a baseline model from the Repository, search for Baseline Predictions Using Most Recent Value and train it on the Baseline Only feature list that has the longest seasonality.

Automatically created feature lists

The following table describes the feature lists automatically created for time series modeling available from the Feature List dropdown:

Feature list Description
All Time Series Features Not actually a feature list, this is the dropdown setting that displays all derived features.
Date Only All features of type Date; used for trend models that only depend on the date.
Time Series Extracted Features A feature list version of All Time Series Features; that is, all derived features.
Time Series Informative Features* All time series features that are considered informative (includes features based on all differencing periods).
Univariate Selections Features that meet a certain threshold for non-linear correlation with the selected target; same as non-time series projects.
Baseline Only (<period>) Naive predictions column matching the period; used for Baseline Predictions blueprints.
No differencing
  • All available naive predictions features
  • Time series features derived using the raw target (not differenced)
  • All other non-target derived features
Target Derived Only Without Differencing (<period>)
  • All available naive predictions features
  • Time series features derived using the raw target (not differenced)
Note that this list is not run by default.
Target Derived Only With Differencing
  • Naive predictions column matching the period
  • Time series features derived using differenced target matching the period
Note that this list is not run by default.
With Differencing (<period>)
  • Naive predictions column matching the period
  • Time series features derived using differenced target matching the period
  • All other non-target derived features
With Differencing (average baseline)
  • Naive predictions using average baseline
  • Target-derived features that capture deviation from the average baseline
  • All other non-target derived features
With Differencing (EWMA baseline)
  • Naive predictions using average baseline with smoothing applied to the baseline
  • Target-derived features that capture deviation from the smoothed average baseline
  • All other non-target EWMA derived features
With Differencing (nonzero average baseline)
  • Naive predictions using nonzero average baseline (zero values are removed when computing the average)
  • Target-derived features that capture deviation from the average baseline
  • Target-derived features that capture lags and statistics of the target flag (whether or not it is zero)
  • All other non-target derived features
With Differencing (intra-month seasonality detection) Multiple feature list options to leverage detected seasonalities (see below).

* The Time Series Informative Features list is not optimal. Preferably, select one of the “with differencing” or the “no differencing” feature lists.

Feature lists for unsupervised time series projects

The following table describes the feature lists automatically created for time series projects that use unsupervised mode (anomaly detection). See the referenced section for details on how DataRobot manages these lists for point anomalies and anomaly windows detection:

Feature list Description
Time Series Extracted Features A feature list version of All Time Series Features; that is, all derived features.
Time Series Informative Features All time series features that are considered informative for time series anomaly detection. For example, DataRobot excludes features it determines are low information or redundant, such as duplicate columns or a column containing empty values.
Actual Values and Rolling Statistics Actual values of the dataset together with the derived statistical information (e.g., mean, median, etc.) of the corresponding feature derivation windows. These features are selected from time series anomaly detection and are applicable to both point anomalies and anomaly windows.
Robust z-score Only Selected rolling statistics from time series derived features but containing only the derived robust z-score values. These features are useful for evaluating point anomalies.
SHAP-based Reduced Features A subset of features based on the Isolation Forest SHAP value scores.
Actual Values Only Selected actual values from the dataset. These features are useful for evaluating point anomalies.
Rolling Statistics Only Selected rolling statistics from time series derived features. These features are useful for evaluating anomaly windows.

Feature lists for Repository blueprints

When building models from the Repository, you can select a specific feature list to run—either the default lists or any lists you created. However, because some blueprints require specific features be present in the feature list, using a feature list without those features can cause model build failure. This may happen, for example, if you created a feature list independent of the model type. To prevent this type of failure, DataRobot checks feature list and blueprint compatibility before starting the model build and returns an error message if appropriate.

Additionally, because DataRobot can identify a preferable feature list type for some blueprints, it suggests that list by default.

Zero-inflated models

When the project target is positive and has at least one zero value, DataRobot always creates a nonzero average baseline feature list and uses it to build optimized zero-inflated models to reflect the data. These models may provide higher accuracy because the specialized algorithms model the zero and count distributions separately.

The nonzero average baseline feature list, with differencing, appends (nonzero) or (is zero) to the target name. Specifically:

  • For (nonzero): features are derived by treating any zero target value as an instance of a missing value.
  • For (is zero): features are derived by substituting target values with a boolean flag (whether the target is zero or not).

The transformed target values ("<target> (nonzero)" and "<target> (is zero)" are not used in modeling. To avoid target leakage during modeling, DataRobot only uses derived transformed target values (lags and statistics). In addition, the "With Differencing (nonzero average baseline)" feature list is only used for zero-inflated model blueprints, which are prefixed with "Zero-Inflated" (for example, Zero-Inflated eXtreme Gradient Boosted Trees Regressor). Note that not all model types have a zero-inflated counterpart.

Zero-inflated modeling considerations

When working with the zero-inflated model and/or feature list, keep the following in mind:

  • You can use the zero-inflated feature list to train on nonzero-inflated models and expect decent (if not optimum) performance.

  • If you use a different feature list to retrain a zero-inflated model, model performance may be poor since the model expects the target derived features in log scale.

Intra-month seasonality detection

Intra-month seasonality is the periodic variation that repeats in the same day/week number or weekday/week number each month. Detecting patterns in seasonality is important for building accurate models—how do you define the date needed from the previous month? Are you counting up from the beginning of the month or down from the end?

Some examples:

Repeat patterns Time unit Example
Same day of month Day A payment is due on a specific day of the month— "payment due on the 15th."
Same week of month and day of week Day Payday is on a certain position within the month—"payday is the second Friday."
Week of month Week High sales for a retail dataset the last week of each month—"sales quota for the month is calculated on the last day."

To provide better handling of seasonality, DataRobot detects and generates appropriate feature lists and then resulting features. These additions are based on whether, when executing the feature engineering that creates the modeling dataset, DataRobot detects intra-month seasonality and a Feature Derivation Window greater than a certain threshold. The feature lists run by Autopilot are based on the characteristics of the data, as described in the table below.

Note

"FDW covers at least X days" is equal to fdw_end - fdw_start >= X.

Condition Description Example
With Differencing (monthly)
Detected intra-month seasonality and feature derivation window covers at least 31 days
  • Naive predictions column matching the period (align to the beginning of the month)
  • Time series features derived using the differenced target matching the period (align to the beginning of the month)
  • All other non-target derived features
Use the first Nth day of the previous month target value as the prediction of the first Nth day of current month—March 5th will use the target value of Feb 5th. Or, in the case of March 30th, the list will use the value of Feb 28 (the last day of February).
With Differencing (monthly, same day from end)
Detected intra-month seasonality and minimum feature derivation window covers at least 31 days
  • Naive predictions column matching the period (align to the end of the month)
  • Time series features derived using the differenced target matching the period (align to the end of the month)
  • Use the last Nth day of the previous month target value as the prediction of the last Nth day of current month—March 31st will use the target value of Feb 28th (or February 29 in leap years).
With Differencing (monthly, same day of week, same week from start)
Detected intra-month seasonality, FDW start ≥ 35, FDW end ≤ 21, FDW window covers at least 29 days
  • Naive predictions column matching the period (align to the week of the month and weekday)
  • Time series features derived using the differenced target matching the period (align to the week number and weekday)
  • All other non-target derived features
Use the target of the first X-day of last month as the prediction of the first X-day of the current month—March 5th (Monday) will use the target value of the February Monday that falls between February 1-7.
With Differencing (monthly, same day of week, same week from end)
Detected intra-month seasonality, FDW start ≥ 35, FDW end ≤ 21, FDW window covers at least 29 days
  • Naive predictions column matching the period (align to the weekday and the "week of the month from the end of the month")
  • Time series features derived using the differenced target matching the period (align to the week number and weekday)
  • All other non-target derived features
Use the target of the last X-day of last month as the prediction of the last X-day of current month—March 31st (Tuesday) will use the target value of the February Tuesday that falls between February 22-28.
With Differencing (monthly, average of previous month)
Detected intra-month seasonality, FDW start ≥ 62, FDW end ≤ 21, FDW window covers at least 29 days
  • Naive predictions using average of the previous month
  • Target-derived features that capture deviation from the previous month’s average baseline
  • All other non-target derived features
Use the average target value of the previous month as the naive prediction of days in the next month—June 7 will use May 1-30 or the average target value of February as the naive prediction of days in March. (Requires a longer FDW.)
With Differencing (monthly, average of same week of previous month)
Detected intra-month seasonality, FDW start ≥ 37, FDW end ≤ 21, FDW window covers at least 29 days
  • Naive predictions using weekly average of the previous month
  • Target-derived features that capture deviation from the previous month’s weekly average baseline
  • All other non-target derived features
Use the first week average of last month as the predictions of the first week of the current month--see below for detail.
With Differencing (monthly, average of nonzero values of previous month)
Detected intra-month seasonality, FDW start ≥ 62, FDW end ≤ 21, FDW window covers at least 29 days with minimum target value of 0
  • Naive predictions using nonzero average of the previous month (zero values are removed in computing the average)
  • Target-derived features that capture deviation from the previous month nonzero average baseline
  • Target-derived features that capture lags and statistics of the target flag (whether or not it is zero)
  • All other non-target derived features
Use the average nonzero target value of February as the naive prediction of days in March.
With Differencing (previous week of the month nonzero values average baseline)
Detected intra-month seasonality, minimum FDW start ≥ 37, maximum FDW end ≤ 21, FDW window covers at least 29 days with minimum target value of 0
  • Naive predictions using weekly nonzero average of previous month (zero values are removed in computing the average)
  • Target-derived features that capture deviation from the previous month weekly nonzero average baseline
  • Target-derived features that capture lags and statistics of the target flag (whether or not it is zero)
  • All other non-target derived features
Use weekly nonzero average of previous month--see below for detail.

Monthly, average of same week from start of previous month

The following details calculations for näive prediction for March:

  1. Compute the weekly average from the start of the month:

    • March 1-7 uses the average value of February 1-7...March 22-31 (last day of month) uses the average value of February 22-28. This feature is called y (last month weekly average).
  2. Compute the weekly average from the end of the month:

    • March 25-31 uses the average value of February 22-28...March 1-10 uses the average value of February 1-7. This feature is called y (match end of the month) (last month weekly average).
  3. Compute the average of the above two features to calculate the naive predictions of the current month:

    • 0.5 * y (last month weekly average) + 0.5 * y (match end of the month) (last month weekly average)

Monthly, average nonzero values in same week from start

The following details calculations for naive prediction for March for nonzero values:

  1. Compute the weekly nonzero average from the start of the month:

    • March 1-7 uses the nonzero average value of February 1-7...March 22-31 (last day of month) uses the nonzero average value of February 22-28. This feature is called y (nonzero)(last month weekly average).
  2. Compute the weekly nonzero average from the end of the month:

    • March 25-31 uses the nonzero average value of February 22-28...March 1-10 uses the nonzero average value of February 1-7. This feature is called y (nonzero)(match end of the month) (last month weekly average).
  3. Compute the average of the above two features to compute the naive predictions of the current month:

    • 0.5 * y (nonzero)(last month weekly average) + 0.5 * y (nonzero)(match end of the month) (last month weekly average)

Updated November 30, 2021
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