An Adaptive Learning Time Series Forecasting Model Based on Decoder Framework

Time series forecasting constitutes a fundamental technique for analyzing dynamic alterations within temporal datasets and predicting future trends in various domains. Nevertheless, achieving effective modeling faces challenges arising from complex factors such as accurately capturing the relationships among temporally distant data points and accommodating rapid shifts in data distributions over time. While Transformer-based models have demonstrated remarkable capabilities in handling long-range dependencies recently, directly applying them to address the evolving distributions within temporal datasets remains a challenging task. To tackle these issues, this paper presents an innovative sequence-to-sequence adaptive learning approach centered on decoder framework for addressing temporal modeling tasks. An end-to-end deep learning architecture-based Transformer decoding framework is introduced, which is capable of adaptively discerning the interdependencies within temporal datasets. Experiments carried out on multiple datasets indicate that the time series adaptive learning model based on the decoder achieved an overall reduction of 2.6% in MSE (Mean Squared Error) loss and 1.8% in MAE (Mean Absolute Error) loss when compared with the most advanced Transformer-based time series forecasting model.