🤖 AI Summary
Traditional methods for nonstationary time series modeling suffer from inaccurate dynamic characterization due to their assumption of stationary statistical properties. To address this, we propose AdaWaveNet—a novel adaptive wavelet network based on a learnable lifting scheme. Its core innovation lies in the first end-to-end integration of the lifting wavelet transform into a deep neural network, enabling data-driven, multiscale wavelet decomposition and reconstruction while overcoming the representational limitations of fixed wavelet bases. The model adopts a differentiable encoder–decoder architecture, unifying support for forecasting, missing value imputation, and super-resolution. Joint multitask training further enhances generalization. Evaluated across 10 benchmark datasets and three task categories, AdaWaveNet consistently outperforms state-of-the-art methods, achieving significant improvements in forecasting accuracy, imputation robustness, and super-resolution quality.
📝 Abstract
Time series data analysis is a critical component in various domains such as finance, healthcare, and meteorology. Despite the progress in deep learning for time series analysis, there remains a challenge in addressing the non-stationary nature of time series data. Traditional models, which are built on the assumption of constant statistical properties over time, often struggle to capture the temporal dynamics in realistic time series, resulting in bias and error in time series analysis. This paper introduces the Adaptive Wavelet Network (AdaWaveNet), a novel approach that employs Adaptive Wavelet Transformation for multi-scale analysis of non-stationary time series data. AdaWaveNet designed a lifting scheme-based wavelet decomposition and construction mechanism for adaptive and learnable wavelet transforms, which offers enhanced flexibility and robustness in analysis. We conduct extensive experiments on 10 datasets across 3 different tasks, including forecasting, imputation, and a newly established super-resolution task. The evaluations demonstrate the effectiveness of AdaWaveNet over existing methods in all three tasks, which illustrates its potential in various real-world applications.