Some variation of COBRA in sequential learning setup
This work addresses forecasting challenges in financial and energy domains, but appears incremental as it builds on existing combined regression strategies.
The paper tackles multivariate time series forecasting by introducing variations of a combined regression strategy with specific data preprocessing, showing that the proposed methodologies outperform all state-of-the-art comparative models across eight datasets from cryptocurrency, stock index, and short-term load forecasting categories.
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.