MWDINet: A multilevel wavelet decomposition interaction network for stock price prediction

Stock price prediction is a classical interdisciplinary issue drawn from finance, computer science, econometrics, and mathematics. Most stock price data are nonlinear, nonstationary, and highly complex, making stock price prediction challenging. Recently, deep neural networks (DNNs) have demonstrated powerful learning capabilities and have yielded notable results in stock price prediction tasks. Most existing deep learning solutions, however, only consider time-domain information or lack effective modeling of frequency-domain information, thus failing to effectively utilize both time-domain and frequency-domain information of the data. Meanwhile, existing methods ignore autocorrelated errors in the stock price forecasting task due to missing valid information data, i.e., they do not consider the correlation between the error at the current time step and the error at the previous time step, which undermines the standard maximum likelihood estimation (MLE) assumption, thereby weakening the model’s performance. We propose a multilevel wavelet decomposition interaction network (MWDINet), an end-to-end framework for stock price prediction. MWDINet employs the multiscale wavelet decomposition interaction module (MWDI-Block) and the Hull Moving Average module (HMA-Block) to extract the data’s frequency-domain and time-domain information, respectively. In MWDI-Block, the traditional signal processing method of Maximum Overlapping Discrete Wavelet Transform (MODWT) is seamlessly embedded into a deep learning framework (named DMODWT). The DMODWT algorithm not only automatically extracts the frequency-domain information from the data, but also fine-tunes the wavelet filters. With HMA-Block, we improve the Hull Moving Average (HMA), commonly used in the industry, into a deep learning module, which learns how changes in different markets over time. Inspired by the research on correcting autocorrelated errors in linear models in econometrics, we further design a deep difference module (DIF-Block) to correct autocorrelated errors and thus improve the prediction performance of the model. Moreover, all components are integrated seamlessly in a unified end-to-end framework. Extensive experiments on real-world datasets demonstrate that MWDINet outperforms the state-of-the-art models and has remarkable potential in stock price prediction.