Does Driverless AI from H2O.ai support mutivariate time series analysis?

Question

Does Driverless AI support mutivariate time series analysis?

I'm trying to do a Time Series Analysis Anomaly forecasting where I need to forecast the spikes in Incident Management ticket Count based upon the Geography (location) and the Type of ticket.

Answer 1

Yes, it does. When modeling with multivariate time series time series of interest is a target while the other time series will be used to make predictions. Data format looks exactly like in this example (not specific to Driverless AI) or see H2O.ai docs for concrete time series example where target time series is Weekly_Sales and other time series variables are Temperature and Fuel_Price.

There is a couple of settings relevant to multivariate time series setup:

• Unavailable at Prediction Time: specify the other time series columns (besides target column) that will only have lag-related features created with it.
• Probability to Create Non-Target Lag Features: specify a probability value for creating non-target lag features (any value between 0 and 1). With multivariate time series this value may go as high as 0.9 or even 1 if no target lag should be used for predictions.

UPDATE

In the spirit of the question Multivariate vs Multiple time series adding more information on modeling time series with Driverless AI. It also supports multiple time series (vs. multivariate time series above) using time groups columns (TGC). In fact, any time series dataset is automatically parsed to identify such groups (alternatively, TGC are specified explicitly by user). Treating TGC as categorical Driverless AI constructs multiple time series - one for each unique combination of values in TGC.

The following settings let user refine how time series model utilizes TGC:

• Consider TGC as Standalone Features
• Which TGC Feature Types to Consider as Standalone Features
• Always Group by All Time Groups Columns for Creating Lag Features

TGC feature operates in combination with multivariate time series, so for each group Driverless AI maintains multivariate times series functionality as described above.