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Derivation Window Cannot Exceed Around 150 Days?

Derivation Window Cannot Exceed Around 150 Days?

I downloaded exactly one year's worth of data to get a good view of the Seasonality within my current analyses. That would be around 365 days. However, DataRobot only allows me to use around 150+ days. How should I identify my Seasonality/Calendar with only about a half a year's worth of data?

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2 Replies

My problem was that I was accepting/thinking the FDW as the amount of data that it would use for the Prediction instead of as the rolling-window. In other words, I was incorrect in saying that if I pulled one year of data to be used as input, it would only use the 150 of the 365 days; not so. The "rolling" window is what it says it is in terms of compartmentalizing the varying runs.

Data Scientist
Data Scientist

Hi Zach,

The derivation window defines the rolling window in creating lagged features: 

The model is not limited to this window to detect seasonality:

So having too long a derivation window is actually the concern that you mentioned for identifying seasonality:

  1. Longer FDW means less training data: with a 150+ day FDW, we'd lose almost half of the 1 year history for the model to learn the trend;
  2. When you select longer FDWs (say, 3 months), DataRobot will automatically derive and test shorter derivation windows (say one week or one month). So 150+ day FDW could take extended time to run.

You can use the API client to build a model factory and test various FDWs. But please align the backtesting periods and forecast windows for an apples to apples comparison. Also, since the data is only 1 year in history, it might be worthwhile to avoid using the month as a feature.