GPTLTS: Utilizing Multi-scale Convolution To Enhance The Long-term Time Series Forecasting With Pre-trained Model

Long-term time series forecasting plays a crucial role in fields such as weather, electricity, and healthcare. However, traditional methods for long-term time series forecasting still face challenges in handling long-term dependencies and nonlinear complex patterns. Pre-trained models are considered state-of-the-art deep learning technology. Recent academic research indicates that these models excel in recognizing and reasoning over complex sequential data. They show significant potential in time series tasks, yet their accuracy in long-term time series forecasting, a cross-modal task, still requires improvement. We propose a novel model named GPTLTS, which integrates GPT-2 for feature extraction from time series data and incorporates multi-scale convolution techniques to enhance feature extraction capabilities. We validate this approach through experiments on multiple time series datasets, demonstrating its effectiveness in capturing complex patterns and long-term dependencies. Experimental results show that the GPTLTS model outperforms traditional methods in time series prediction tasks, achieving higher prediction accuracy and generalization capabilities.