Some variation of COBRA in sequential learning setup
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929337537658880 |
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| author | Bhambu, Aryan Dey, Arabin Kumar |
| author_facet | Bhambu, Aryan Dey, Arabin Kumar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_04539 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Some variation of COBRA in sequential learning setup Bhambu, Aryan Dey, Arabin Kumar Machine Learning Computational Engineering, Finance, and Science Signal Processing Computational Finance 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. |
| title | Some variation of COBRA in sequential learning setup |
| topic | Machine Learning Computational Engineering, Finance, and Science Signal Processing Computational Finance |
| url | https://arxiv.org/abs/2405.04539 |