Time series analysis is of critical importance in a wide range of applications, including weather forecasting, anomaly detection, and action recognition. Accurate time series forecasting requires modeling complex temporal dependencies, particularly multi-scale periodic patterns. To address this challenge, we propose a novel Wavelet-Enhanced Transformer (Wave-Net). Wave-Net transforms 1D time series data into 2D matrices based on periodicity, enhancing the capture of temporal patterns through convolutional filters. This paper introduces Wave-Net, a model that incorporates wavelet and Fourier transforms for feature extraction, along with an enhanced cycle offset and optimized dynamic K for improved robustness. The Transformer layer is further refined to bolster long-term modeling capabilities. Evaluations on real-world benchmarks demonstrate that Wave-Net consistently achieves state-of-the-art performance across mainstream time series analysis tasks.

Time series analysis is of critical importance in a wide range of applications, including weather forecasting, anomaly detection, and action recognition. Accurate time series forecasting requires modeling complex temporal dependencies, particularly multi-scale periodic patterns. To address this challenge, we propose a novel Wavelet-Enhanced Transformer (Wave-Net). Wave-Net transforms 1D time series data into 2D matrices based on periodicity, enhancing the capture of temporal patterns through convolutional filters. This paper introduces Wave-Net, a model that incorporates wavelet and Fourier transforms for feature extraction, along with an enhanced cycle offset and optimized dynamic K for improved robustness. The Transformer layer is further refined to bolster long-term modeling capabilities. Evaluations on real-world benchmarks demonstrate that Wave-Net consistently achieves state-of-the-art performance across mainstream time series analysis tasks.