Some variation of COBRA in sequential learning setup
CoRR(2024)
Abstract
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.
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