Exploring different modelling approaches for time series forecasting

Approach 1: Using XGBoost with lagged values of the time series

An optimal number of time lags is determined for forecasting the 1 step ahead forecast for a given time series using XGBoost.

Approach 2: Deep learning using LSTM

Implementation using LSTM with PyTorch.