CaReTS: A Multi-Task Framework Unifying Classification and Regression for Time Series Forecasting
arXiv:2511.09789v2 Announce Type: replace 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 dev
The paper, replacing a previous version, showcases continued academic advancement in time series forecasting, crucial for various AI applications. It reflects ongoing efforts to improve model accuracy and interpretability in dynamic predictions.
Improved time series forecasting directly impacts AI agent capabilities, financial markets, logistics, and resource management by enabling more accurate predictions and actionable insights.
This framework offers a more robust method for combining classification and regression in time series, potentially leading to more reliable and transparent predictive models.
- · AI developers
- · Analytics software companies
- · Finance sector
- · Logistics and supply chain management
- · Traditional statistical forecasting methods
- · Companies with less sophisticated predictive analytics
More accurate and interpretable AI predictions for complex time-dependent data.
Enhanced decision-making in sectors like finance and manufacturing due to better forecasting accuracy.
The development of more autonomous and reliable AI agents that can operate with greater foresight within dynamic environments.
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Read at arXiv cs.LG