A Multivariate Short-Term Trend Information-Based Time Series Forecasting Algorithm for PM 2.5 Daily Concentration Prediction

PM 2.5 concentration prediction is a hot topic in atmospheric environment research and management. In this study, we adopt an extended dynamics differentiator and regression model to construct the novel multivariate short-term trend information-based time series forecasting algorithm (M-STI-TSF) to tackle this issue. The advantage of this model is that the dynamical short-term trend information, based on tracking-differentiator, is insensitive to high-frequency noise and is complementary to traditional statistical information. Due to the fact that the dynamical short-term trend information provided by the tracking-differentiator can effectively describe the trend of time series fluctuations, it greatly supplements the empirical information of the prediction system. It cannot be denied that short-term trend information is an effective way to improve prediction accuracy. The modeling process can be summarized as the following main steps. Firstly, each one-dimensional time series composed of an input feature is predicted using a dynamical prediction model, including short-term trend information. Then, the predicted results of multiple one-dimensional influence factors are linearly regressed to obtain the final predicted value. The simulation experiment selected major cities in North China as the research object to demonstrate that the proposed model performs better than traditional models under different model generalization ability evaluation indexes. The M-STI-TS model successfully extracted the inherent short-term trend information of PM 2.5 time series, which was effectively and reasonably integrated with traditional models, resulting in significantly improved prediction accuracy. Therefore, it can be proven that the short-term trend information extracted by tracking-differentiator not only reflects the intrinsic characteristics of time series for practical applications, but also serves as an effective supplement to statistical information.