Enhancing Forecasting with a 2D Time Series Approach for Cohort-Based Data
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909746871664640 |
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| author | Guttel, Yonathan Moradov, Orit Lieder, Nachi Greenstein-Messica, Asnat |
| author_facet | Guttel, Yonathan Moradov, Orit Lieder, Nachi Greenstein-Messica, Asnat |
| contents | This paper introduces a novel two-dimensional (2D) time series forecasting model that integrates cohort behavior over time, addressing challenges in small data environments. We demonstrate its efficacy using multiple real-world datasets, showcasing superior performance in accuracy and adaptability compared to reference models. The approach offers valuable insights for strategic decision-making across industries facing financial and marketing forecasting challenges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_15369 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Enhancing Forecasting with a 2D Time Series Approach for Cohort-Based Data Guttel, Yonathan Moradov, Orit Lieder, Nachi Greenstein-Messica, Asnat Machine Learning This paper introduces a novel two-dimensional (2D) time series forecasting model that integrates cohort behavior over time, addressing challenges in small data environments. We demonstrate its efficacy using multiple real-world datasets, showcasing superior performance in accuracy and adaptability compared to reference models. The approach offers valuable insights for strategic decision-making across industries facing financial and marketing forecasting challenges. |
| title | Enhancing Forecasting with a 2D Time Series Approach for Cohort-Based Data |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.15369 |