TimesNet-PM2.5: Interpretable TimesNet for Disentangling Intraperiod and Interperiod Variations in PM2.5 Prediction

Abstract

Time-series forecasting has a wide range of application scenarios. Predicting particulate matter with a diameter of 2.5 μm or less (PM2.5) in the future is a vital type of time-series forecasting task where valid forecasting would provide an important reference for public decisions. The current state-of-the-art general time-series model, TimesNet, has achieved a level of performance well above the mainstream level on most benchmarks. Attributing this success to an ability to disentangle intraperiod and interperiod temporal variations, we propose TimesNet-PM2.5. To make this model more powerful for concrete PM2.5 prediction tasks, task-oriented improvements to its structure have been added to enhance its ability to predict specific time spots through better interpretability and meaningful visualizations. On the one hand, this paper rigorously investigates the impact of various meteorological indicators on PM2.5 levels, examining their primary influencing factors from both local and global perspectives. On the other hand, using visualization techniques, we validate the capability of representation learning in time-series forecasting and performance on the forecasting task of the TimesNet-PM2.5. Experimentally, TimesNet-PM2.5 demonstrates an improvement over the original TimesNet. Specifically, the Mean Squared Error (MSE) improved by 8.8% for 1-h forecasting and by 22.5% for 24-h forecasting.

Keywords

• PM2.5 forecasting
• time series
• interpretability
• machine learning
• deep learning