I would like to predict next day close price, df[‘close’]
I know that the data should be normalized before use in a model, but just trying to get a gluonts compatible dataset first.
My problem remains that of the various ways I can make a gluonts dataset, none of them create a dataset that makes sense to me for use in a model.
I could be wrong, but I think what makes most sense is a structure like:
Train:{
“start”:[timestamp, freq=freq],
“target”:[*close price on day of “start”],
“feat_static_cat”:[*static features of current time series, exchange, ticker, asset_type],
“feat_dynamic_cat”:[*dynamic features of current time sereies-
that change through time but should be used-
ex: sine, indicator, HLCV %chg, ect.],
“item_id”:[unique identifier of some sort]
}Where there is a train_ds for every value of time in the time series, ie there should be as many x in train_ds as there are rows in the original dataframe.