# Some variation of COBRA in sequential learning setup

arXiv: https://arxiv.org/abs/2405.04539 · HTML: https://mlsanity.com/paper/2405.04539/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 7 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Recurrent Highway Networks](https://mlsanity.com/paper/1607.03474/) (2016)
- [COBRA: A Combined Regression Strategy](https://mlsanity.com/paper/1303.2236/) (2013)

## Abstract (arXiv)

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.