DiffMixer: A prediction model based on mixing different frequency features.

Abstract

Time series forecasting is widely applied in fields such as energy and network security. Various prediction models based on Transformer and MLP architectures have been proposed. However, their performance may decline to varying degrees when applied to real-world sequences with significant non-stationarity. Traditional approaches generally adopt either stabilization or a combination of stabilization and non-stationarity compensation for prediction tasks. However, non-stationarity is a crucial attribute of time series; the former approach tends to eliminate useful non-stationary patterns, while the latter may inadequately capture non-stationary information. Therefore, we propose DiffMixer, which analyzes and predicts different frequencies in non-stationary time series. We use Variational Mode Decomposition (VMD) to obtain multiple frequency components of the sequence, Multi-scale Decomposition (MsD) to optimize the decomposition of downsampled sequences, and Improved Star Aggregate-Redistribute (iSTAR) to capture interdependencies between different frequency components. Additionally, we employ the Frequency domain Processing Block (FPB) to capture global features of different frequency components in the frequency domain, and Dual Dimension Fusion (DuDF) to fuse different frequency components in two dimensions, enhancing the predictive fit for various frequencies. Compared to previous state-of-the-art methods, DiffMixer reduces the Mean Squared Error (MSE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Symmetric Mean Absolute Percentage Error (SMAPE) by 24.5%, 12.3%, 13.5%, and 6.1%, respectively.