CC-Time: Cross-Model and Cross-Modality Time Series Forecasting
This work addresses the challenge of leveraging PLMs for time series forecasting, which is important for applications in fields like finance and healthcare, but it appears incremental as it builds on existing PLM-based methods.
The paper tackles the problem of improving time series forecasting accuracy using pre-trained language models (PLMs) by proposing CC-Time, which incorporates cross-modality learning and cross-model fusion to better model temporal dependencies and channel correlations, achieving state-of-the-art results on nine real-world datasets.
With the success of pre-trained language models (PLMs) in various application fields beyond natural language processing, language models have raised emerging attention in the field of time series forecasting (TSF) and have shown great prospects. However, current PLM-based TSF methods still fail to achieve satisfactory prediction accuracy matching the strong sequential modeling power of language models. To address this issue, we propose Cross-Model and Cross-Modality Learning with PLMs for time series forecasting (CC-Time). We explore the potential of PLMs for time series forecasting from two aspects: 1) what time series features could be modeled by PLMs, and 2) whether relying solely on PLMs is sufficient for building time series models. In the first aspect, CC-Time incorporates cross-modality learning to model temporal dependency and channel correlations in the language model from both time series sequences and their corresponding text descriptions. In the second aspect, CC-Time further proposes the cross-model fusion block to adaptively integrate knowledge from the PLMs and time series model to form a more comprehensive modeling of time series patterns. Extensive experiments on nine real-world datasets demonstrate that CC-Time achieves state-of-the-art prediction accuracy in both full-data training and few-shot learning situations.