STRATA-TS: Selective Knowledge Transfer for Urban Time Series Forecasting with Retrieval-Guided Reasoning

• URL: http://arxiv.org/abs/2508.18635v2
• Date: Mon, 22 Sep 2025 12:00:11 GMT
• Title: STRATA-TS: Selective Knowledge Transfer for Urban Time Series Forecasting with Retrieval-Guided Reasoning
• Authors: Yue Jiang, Chenxi Liu, Yile Chen, Qin Chao, Shuai Liu, Cheng Long, Gao Cong,
• Abstract summary: STRATA-TS is a framework that combines domain-adapted retrieval with reasoning-capable large models to improve forecasting in scarce data regimes.<n>To enable efficient deployment, we distill the reasoning process into a compact open model via supervised fine-tuning.<n>Experiments on three parking availability datasets across Singapore, Nottingham, and Glasgow demonstrate that STRATA-TS consistently outperforms strong forecasting and transfer baselines.
• Score: 27.775793400546345
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Urban forecasting models often face a severe data imbalance problem: only a few cities have dense, long-span records, while many others expose short or incomplete histories. Direct transfer from data-rich to data-scarce cities is unreliable because only a limited subset of source patterns truly benefits the target domain, whereas indiscriminate transfer risks introducing noise and negative transfer. We present STRATA-TS (Selective TRAnsfer via TArget-aware retrieval for Time Series), a framework that combines domain-adapted retrieval with reasoning-capable large models to improve forecasting in scarce data regimes. STRATA-TS employs a patch-based temporal encoder to identify source subsequences that are semantically and dynamically aligned with the target query. These retrieved exemplars are then injected into a retrieval-guided reasoning stage, where an LLM performs structured inference over target inputs and retrieved support. To enable efficient deployment, we distill the reasoning process into a compact open model via supervised fine-tuning. Extensive experiments on three parking availability datasets across Singapore, Nottingham, and Glasgow demonstrate that STRATA-TS consistently outperforms strong forecasting and transfer baselines, while providing interpretable knowledge transfer pathways.

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