Solved: AutoML forecasting: understanding the rolling fore... - Page 2

lambdalove · 01-31-2023 01:26 PM

I trained a time series forecasting model with AutoML. During training, I checked the option to "Export test dataset to BigQuery." I have a question about how to understand the data that appears in the exported table.

My understanding is that a "predicted_on" timestamp is essentially the first date of the forecast horizon of a rolling forecast window. I see that for each "predicted_on" timestamp, there are 6 timestamps from the test data split of my training data. This suggests that the forecast horizon is 6 weeks long; i.e., for each "predicted_on" date (and starting on that date) it predicts 6 weeks of data.

My question is, where does the number 6 come from? (When I trained the model, I specified that the forecast horizon is 26 weeks, not 6...)

lambdalove

To answer my question:

I'm not sure where the number 6 came from, but I've since discovered the following.

The timestamp format I was using is not among the timestamp formats supported by Google according to this documentation. I changed the format of my timestamps. I also ensured that every number in my target column has a decimal (it was previously a mix of integers and decimal numbers).

After making these changes, I trained a new model and examined the data exported to BigQuery.

Now I see that there are 26 weeks of timestamps from the test data split associated with the first `predicted_on` timestamp. This would suggest that the forecast horizon of the rolling forecast window is 26 weeks long. This is what I would expect, given that I set the forecast horizon to 26 when I trained the model.

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AutoML forecasting: understanding the rolling forecast window during model evaluation