I have a time series daily data for about 6 years(1.8k data points). I am trying to forecast the next t+30 values, Train data independent matrix (X)=Sequences of previous 30 day values Train (Y)=The 31st day value for each of previous 30 day values.
I followed the following methodology to forecast: Y for t+1 is first forecasted, Then X matrix row is shifted by 1 day and the forecasted Y is appended to the end of this row, then use this row to predict t+2 value and continue. However in each sequence after (usually) t+3 days the forecasted values become constant for the rest of the t+n days.
Is this the correct way to forecast time series with LSTMs?
How can this behaviour be explained?
Will this be the case even for a time series with great seasonality?
Is this behaviour expected even for a very large time series data?
Should I rather try to train the network with Y matrix having 31st day to 60th day values for the same X?
My train data looks something like this:
array([[-0.35811423, -0.22393472, -0.39437897, ..., -0.36718042, -0.37080689, -0.35267452], [-0.22393472, -0.39437897, -0.13327289, ..., -0.37080689, -0.35267452, -0.2030825 ], [-0.39437897, -0.13327289, -0.1532185 , ..., -0.35267452, -0.2030825 , -0.25294651]
Architecture: Input LSTM layer (20 neurons) 1 hidden lstm layer 20 neurons 1 output dense layer, batch size as 1. Stateful lstm, I reset model states after each epoch.