Does shuffling data for time series forecasting help? 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_loss. 

Since 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?
Shuffling data would not seem to make sense here, since your model has "memory". You're not predicting $y_i$ from only $x_i$, but also $x_{i-1}$ and $x_{i-2}$. If you shuffle the data and perform prediction, you are implying that $x_1, x_2, x_3$ should give the same value as $x_2, x_1, x_3$ or $x_9, x_5, x_3$, or any series of values that merely ends in $x_3$ (since the target value is always $y_3$, regardless of the other $x$). If your target value actually does depend on preceding variables, shuffling the data breaks that relationship. If it does not depend on preceding values, it's arguably not a time-series model, since the ordering of observations is irrelevant.