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What is feat_dynamic_real, feat_static_cat and feat_static_real in gluonTS models? When do we use these features while running the model? Can someone provide an example for the same?

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feat_dynamic_real : dynamic (over time) real features, an array with shape equal to (number of features, target length). if the target time series represents the demand for different products, an associated dynamic_feat might be a boolean time series which indicates whether a promotion was applied (1) to the particular product or not (0)

feat_static_cat: static (over time) categorical features, a list with dimension equal to the number of features. You can use an array of categorical features to encode the groups to which the record belongs. Categorical features must be encoded as a 0-based sequence of positive integers. For example, the categorical domain {R, G, B} can be encoded as {0, 1, 2}. You must represent all values from each categorical domain in the training dataset. That's because the DeepAR algorithm can forecast only for categories that have been observed during training

feat_static_real: static (over time) real features, a list with dimension equal to the number of features

From gluonTS documentation here

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