Introduction to Time Series

Peter Hurford, Madeleine Mott

2018-01-17

DataRobot now includes the ability to make time series projects via the API.

Time series projects, like OTV projects, use datetime partitioning, and all the workflow changes that apply to other datetime partitioned projects also apply to them. Unlike other projects, time series projects produce different types of models which forecast multiple future predictions instead of an individual prediction for each row.

DataRobot uses a general time series framework to configure how time series features are created and what future values the models will output. This framework consists of a Forecast Point (defining a time a prediction is being made), a Feature Derivation Window (a rolling window used to create features), and a Forecast Window (a rolling window of future values to predict). These components are described in more detail below.

Time series projects will automatically transform the dataset provided in order to apply this framework. During the transformation, DataRobot uses the Feature Derivation Window to derive time series features (such as lags and rolling statistics), and uses the Forecast Window to provide examples of forecasting different distances in the future (such as time shifts). After project creation, a new dataset and a new feature list are generated and used to train the models. This process is reapplied automatically at prediction time as well in order to generate future predictions based on the original data features.

The timeUnit and timeStep used to define the Feature Derivation and Forecast Windows are taken from the datetime partition column, and can be retrieved for a given column in the input data by using GetFeatureInfo.

Setting Up A Time Series Project

To set up a time series project, follow the standard GenerateDatetimePartition workflow and use the six new time series specific parameters found in CreateDatetimePartitionSpecification: :py:class:datarobot.DatetimePartitioning object:

Feature Derivation Window

The Feature Derivation window represents the rolling window that is used to derive time series features and lags, relative to the Forecast Point. It is defined in terms of featureDerivationWindowStart and featureDerivationWindowEnd which are integer values representing datetime offsets in terms of the timeUnit (e.g. hours or days).

The Feature Derivation Window start and end must be less than or equal to zero, indicating they are positioned before the forecast point. Additionally, the window must be specified as an integer multiple of the timeStep which defines the expected difference in time units between rows in the data.

The window is closed, meaning the edges are considered to be inside the window.

Forecast Window

The Forecast Window represents the rolling window of future values to predict, relative to the Forecast Point. It is defined in terms of the forecastWindowStart and forecastWindowEnd, which are positive integer values indicating datetime offsets in terms of the timeUnit (e.g. hours or days).

The Forecast Window start and end must be positive integers, indicating they are positioned after the forecast point. Additionally, the window must be specified as an integer multiple of the timeStep which defines the expected difference in time units between rows in the data.

The window is closed, meaning the edges are considered to be inside the window.

Modeling Data and Time Series Features

In time series projects, a new set of modeling features is created after setting the partitioning options. If a featurelist is specified with the partitioning options, it will be used to select which features should be used to derived modeling features; if a featurelist is not specified, the default featurelist will be used.

These features are automatically derived from those in the project’s dataset and are the features used for modeling - note that ListFeaturelists and ListModelingFeaturelists will return different data in time series projects. Modeling featurelists are the ones that can be used for modeling and will be accepted by the backend, while regular featurelists will continue to exist but cannot be used. Modeling features are only accessible once the target and partitioning options have been set. In projects that don’t use time series modeling, once the target has been set, both modeling and regular features and featurelists will behave the same.

Making Predictions

Prediction datasets are uploaded as normal predictions. However, when uploading a prediction dataset, a new parameter forecastPoint can be specified. The forecast point of a prediction dataset identifies the point in time relative which predictions should be generated, and if one is not specified when uploading a dataset, the server will choose the most recent possible forecast point. The forecast window specified when setting the partitioning options for the project determines how far into the future from the forecast point predictions should be calculated.

When setting up a time series project, input features could be identified as a priori features. These features are not used to generate lags, and are expected to be known for the rows in the forecast window at predict time (e.g. “how much money will have been spent on marketing”, “is this a holiday”).

When uploading datasets to a time series project, the dataset might look something like the following, if “Time” is the datetime partition column, “Target” is the target column, and “Temp.” is an input feature. If the dataset was uploaded with a forecast point of “2017-01-08” and during partitioning the feature derivation window start and end were set to -5 and -3 and the forecast window start and end were set to 1 and 3, then rows 1 through 3 are historical data, row 6 is the forecast point, and rows 7 though 9 are forecast rows that will have predictions when predictions are computed.

row time target temp
1 2017-01-03 16443 72
2 2017-01-04 3013 72
3 2017-01-05 1643 68
4 2017-01-06 NA NA
5 2017-01-07 NA NA
6 2017-01-08 NA NA
7 2017-01-09 NA NA
8 2017-01-10 NA NA
9 2017-01-11 NA NA

On the other hand, if the project instead used “Holiday” as an a priori input feature, the uploaded dataset might look like the following.

row time target holiday
1 2017-01-03 16443 TRUE
2 2017-01-04 3013 FALSE
3 2017-01-05 1643 FALSE
4 2017-01-06 NA FALSE
5 2017-01-07 NA FALSE
6 2017-01-08 NA FALSE
7 2017-01-09 NA TRUE
8 2017-01-10 NA FALSE
9 2017-01-11 NA FALSE