  This research paper introduces innovative approaches for multivariate time
series forecasting based on different variations of the combined regression
strategy. We use specific data preprocessing techniques which makes a radical
change in the behaviour of prediction. We compare the performance of the model
based on two types of hyper-parameter tuning Bayesian optimisation (BO) and
Usual Grid search. Our proposed methodologies outperform all state-of-the-art
comparative models. We illustrate the methodologies through eight time series
datasets from three categories: cryptocurrency, stock index, and short-term
load forecasting.
