CODEWITHSUNDEEP
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theHall
theHall

Reputation: 43

Time Series forecasting with DeepAR for multiple independent products

I wanted to forecast some data(suppose countries temperature).Is there any way to add multiple countires temperature at once in deepAR (Algorithm available at AWS Sagemaker marketplace) and deepAR forecast them independently?.Is it possible to remove a particular country data and add another after few days?

I am new to Forecasting and wanted to try deepAR.If anyone has arleady worked on this, please provide me some guidelines on how to do this using deepAR

Link - https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html

Upvotes: 2

Views: 1770

Answers (1)

OscarVanL
OscarVanL

Reputation: 719

This is a late reply to this post, but my reply could be helpful in the future to others. The answer to your first question is yes.

The page you linked to references the cat field, this allows you to encode a vector representing different record groups. In your case, the cat field can just be a single value, but the cat field can encode more complex relationships too with more dimensions in the vector.

Say you have 3 countries you want to make predictions on. You have some time-series temperature training data for each country, then you would enter them as rows in the train JSON file like this:

Country 1:

{"start": "02/09/2019 00:00:00", "target": [T1,T2,T3,T4,...], "cat": [0]}

Country 2:

{"start": "02/09/2019 00:00:00", "target": [T1,T2,T3,T4,...], "cat": [1]}

Country 3:

{"start": "02/09/2019 00:00:00", "target": [T1,T2,T3,T4,...], "cat": [2]}

The category field indicates to DeepAR that these are independent data categories, in other words, different countries.

The frequency (time between temperature measurements) has to be the same for all data, however, the start time and the number of training points does not.

When you've trained the model, open the endpoint and want to make a prediction for a country, you can pass the context for a particular country along with the same cat as one of those countries above. This allows you to make a single model that will allow you to make predictions from many independent groups of data.

I'm not sure exactly what you mean by the second question. If you mean to add more training data for another country later on, this would require you to create a different training dataset with an additional category for that country, then re-train the model.

Upvotes: 4

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