Does shuffling data for time series forecasting help?
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.