CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting

• URL: http://arxiv.org/abs/2511.09789v1
• Date: Fri, 14 Nov 2025 01:09:54 GMT
• Title: CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting
• Authors: Fulong Yao, Wanqing Zhao, Chao Zheng, Xiaofei Han,
• Abstract summary: CaReTS is a novel multi-task learning framework that combines classification and regression tasks.<n>Four variants (CaReTS1--4) are instantiated under this framework to incorporate mainstream temporal modelling encoders.<n>Experiments on real-world datasets demonstrate that CaReTS outperforms state-of-the-art (SOTA) algorithms in forecasting accuracy.
• Score: 7.600702118055632
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in deep forecasting models have achieved remarkable performance, yet most approaches still struggle to provide both accurate predictions and interpretable insights into temporal dynamics. This paper proposes CaReTS, a novel multi-task learning framework that combines classification and regression tasks for multi-step time series forecasting problems. The framework adopts a dual-stream architecture, where a classification branch learns the stepwise trend into the future, while a regression branch estimates the corresponding deviations from the latest observation of the target variable. The dual-stream design provides more interpretable predictions by disentangling macro-level trends from micro-level deviations in the target variable. To enable effective learning in output prediction, deviation estimation, and trend classification, we design a multi-task loss with uncertainty-aware weighting to adaptively balance the contribution of each task. Furthermore, four variants (CaReTS1--4) are instantiated under this framework to incorporate mainstream temporal modelling encoders, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and Transformers. Experiments on real-world datasets demonstrate that CaReTS outperforms state-of-the-art (SOTA) algorithms in forecasting accuracy, while achieving higher trend classification performance.

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