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Hi @marah othman. By period do you mean the forecast horizon?

yes how can i choose this period in general

The forecast horizon usually depends on the application and task

we also use this period in the past right to train ? or not

Yes, models are trained based on the train loss, which depends on the error on the forecasting window, determined by the horizon

or there any constrain also on the how i should put data to train my models

Models are usually trained on historic data of your variable of interest. You should have a longer history than the horizon. During training, the models are internally creating smaller windows of size

input_size+h

. Using the input window if size

input_size

to forecast the future of size

h

but what about how much can i predict if i have three month for trained data

the horizon must be specified before training the model, models specialize on forecasting that particular horizon. You can't increase it afterwards. What frequency is your data?

every hour

With 3 months of data you might have enough to forecast up to a day, or even week. The performance usually degrades for longer horizons, it is empirical and highly dependent on the data.

yes if i did for loop and connect the data and stored and predict for h is this reasonable ?

Yes, recursive forecasts are a valid and largely used technique. However, we have observed that directly forecasting everything with a large

h

performs better. The performance of you recursive approach might sharply decrease after a couple of iterations due to error accumulation.

depend on what i should choose the input size ?

As a very general rule of thumb, we usually try different input sizes ranging from 2*h to 5*h

so what do you mean by input-size+h