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?](https://datascience.stackexchange.com/questions/54237/is-it-valid-to-shuffle-time-series-data-for-a-prediction-task)