So I am trying time series forecasting using LSTM's. The aim is to predict $Y$ given $X$ using regression.
I had already converted the input data into a sliding window format such that if my input data was of the form:
X = [x0, x1, x2,.....] Y = [y0, y1, y2,.....]
Then I converted it into:
Xnew = [(x0, x1, x2), (x1, x2, x3), (x2, x3, x4),...] Y = [ y2, y3, y4,...]
Still, upon training my data I find a very high
validation_split takes only the fraction of the data from the end, I thought maybe I should try and randomize the data before training it. However, in that way, will time series have any meaning?
I found a similar question, but I had apparently already tried what was suggested: Is it valid to shuffle time-series data for a prediction task?