A Data-Driven Scale-Adaptive Time-Frequency Convolutional Network for Long Sequence Time-Series Forecasting

Models based on Transformer variants have consistently demonstrated leading performance in long sequence time series forecasting. However, in some complex application scenarios, Transformers tend to capture low-frequency information in the data while overlooking high-frequency information, which often contains rich non-stationary features. This unbalanced feature extraction approach limits the model's ability to effectively handle real-world time series data. To address this issue, we explicitly represent both low-frequency and high-frequency information and propose a model called STCNet, a data-driven scale-adaptive convolutional network that aims to extract diverse features and patterns from the data by learning features across different frequency bands in a balanced manner. Specifically, we propose an entropy-based adaptive wavelet basis selection algorithm, which can adaptively select appropriate wavelet bases based on the data distribution to achieve effective multi-frequency decomposition of complex time series. In addition, we designed a hierarchical scale-adaptive factor that allows for dynamic adjustment of feature weights according to different time scales through refined layered weight adjustment, significantly enhancing the model's capability in handling non-stationary time series features. To further optimize the output features of the model, we introduce a test-time training mechanism, combined with a fast weight update strategy and a weight-sharing strategy to reduce the number of model parameters, effectively mitigating the risk of overfitting. Experimental results on nine datasets demonstrate that STCNet outperforms the current state-of-the-art models in both effectiveness and efficiency.