CC-Time: Cross-Model and Cross-Modality Time Series Forecasting

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. With the rapid growth of the Internet of Things, vast amounts of time series data are being generated, driving increasing interest in time series forecasting (TSF) Kaastra & Boyd (1996); Faloutsos et al. (2018). Current TSF methods primarily design specific modules to exploit the inherent knowledge of the time series data, and achieve good prediction accuracy Liu et al. (2024c); Nie et al. (2023), which we call time-series-specific models in this paper. Recently, pre-trained language models (PLMs) have demonstrated remarkable success across diverse fields Wang et al. (2024); Wu et al. (2024), prompting exploration in TSF Zhou et al. (2023); Jin et al. (2024a). Some approaches attempt to leverage the representation capacity and sequential modeling capability of PLMs to capture time series patterns for TSF, which we call PLM-based models Liu et al. (2024d). Although these methods show good prospects, they have not yet achieved satisfactory prediction accuracy, leaving an under-explored problem of how to effectively activate the potential of PLMs for TSF.