Beyond Trend and Periodicity: Guiding Time Series Forecasting with Textual Cues

• URL: http://arxiv.org/abs/2405.13522v2
• Date: Fri, 24 May 2024 15:10:27 GMT
• Title: Beyond Trend and Periodicity: Guiding Time Series Forecasting with Textual Cues
• Authors: Zhijian Xu, Yuxuan Bian, Jianyuan Zhong, Xiangyu Wen, Qiang Xu,
• Abstract summary: This work introduces a novel Text-Guided Time Series Forecasting (TGTSF) task. By integrating textual cues, such as channel descriptions and dynamic news, TGTSF addresses the critical limitations of traditional methods. We propose TGForecaster, a robust baseline model that fuses textual cues and time series data using cross-attention mechanisms.
• Score: 9.053923035530152
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This work introduces a novel Text-Guided Time Series Forecasting (TGTSF) task. By integrating textual cues, such as channel descriptions and dynamic news, TGTSF addresses the critical limitations of traditional methods that rely purely on historical data. To support this task, we propose TGForecaster, a robust baseline model that fuses textual cues and time series data using cross-attention mechanisms. We then present four meticulously curated benchmark datasets to validate the proposed framework, ranging from simple periodic data to complex, event-driven fluctuations. Our comprehensive evaluations demonstrate that TGForecaster consistently achieves state-of-the-art performance, highlighting the transformative potential of incorporating textual information into time series forecasting. This work not only pioneers a novel forecasting task but also establishes a new benchmark for future research, driving advancements in multimodal data integration for time series models.

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