Enhancing Forecasting with a 2D Time Series Approach for Cohort-Based Data
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 21 Aug 2025.
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8 of 24 references.
References · 8
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
- 2020Time Series Forecasting With Deep Learning: A Surveycited by 459
- 2016Mean Absolute Percentage Error for regression modelscited by 58
- 2013An Introductory Study on Time Series Modeling and Forecastingcited by 23
- 2021NeuralProphet: Explainable Forecasting at Scalecited by 22
- 2021A better measure of relative prediction accuracy for model selection and model estimationcited by 20
- 2023Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022cited by 17
- 2020Low-Rank Autoregressive Tensor Completion for Multivariate Time Series Forecastingcited by 4
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