Taming Pre-trained LLMs for Generalised Time Series Forecasting via Cross-modal Knowledge Distillation

Multivariate time series forecasting has recently gained great success with the rapid growth of deep learning models. However, existing approaches usually train models from scratch using limited temporal data, preventing their generalization. Recently, with the surge of the Large Language Models (LLMs), several works have attempted to introduce LLMs into time series forecasting. Despite promising results, these methods directly take time series as the input to LLMs, ignoring the inherent modality gap between temporal and text data. In this work, we propose a novel Large Language Models and time series alignment framework, dubbed LLaTA, to fully unleash the potentials of LLMs in the time series forecasting challenge. Based on cross-modal knowledge distillation, the proposed method exploits both input-agnostic static knowledge and input-dependent dynamic knowledge in pre-trained LLMs. In this way, it empowers the forecasting model with favorable performance as well as strong generalization abilities. Extensive experiments demonstrate the proposed method establishes a new state of the art for both long- and short-term forecasting. Code is available at \url{https://github.com/Hank0626/LLaTA}.

WFTNet: Exploiting Global and Local Periodicity in Long-term Time Series Forecasting

Recent CNN and Transformer-based models tried to utilize frequency and periodicity information for long-term time series forecasting. However, most existing work is based on Fourier transform, which cannot capture fine-grained and local frequency structure. In this paper, we propose a Wavelet-Fourier Transform Network (WFTNet) for long-term time series forecasting. WFTNet utilizes both Fourier and wavelet transforms to extract comprehensive temporal-frequency information from the signal, where Fourier transform captures the global periodic patterns and wavelet transform captures the local ones. Furthermore, we introduce a Periodicity-Weighted Coefficient (PWC) to adaptively balance the importance of global and local frequency patterns. Extensive experiments on various time series datasets show that WFTNet consistently outperforms other state-of-the-art baseline.