An experimental comparison of classic statistical techniques on univariate time series forecasting

Abstract

In today’s world, there is a high demand for understanding and analyzing patterns in time series data in order to make accurate forecasts and predictions. Multiple univariate time series demonstrating the concentration of various air pollutants such as PM10, PM2.5, NH3, NOx, Ozone, SO2, CO and an air quality index (AQI) were used for this study. The research examines and categorizes many classic statistical forecasting techniques into three groups: smoothing techniques, linear regressive techniques for stationary data, and linear regressive techniques for nonstationary data. Nine different approaches were used in order to comprehend the logic behind and effectiveness of statistical techniques, including the simple, double, and triple exponential smoothing methods, the autoregressive method, the moving average method, the ARMA method, and the ARIMA families of methods, which include the ARIMA, SARIMA, and SARIMAX methods. The RMSE metric is used for evaluation, and it shows that the SARIMAX method outperformed other techniques for PM2.5, PM10, NH3, Ozone, and AQI with RMSE values of 8.56, 6.72, 5.52, 3.51, and 20.88, respectively, whereas for NOx and SO2, SARIMA produced better results with RMSE values of 9.8 and 2.48. Only for CO time series, triple exponential smoothing outperformed with an RMSE of 0.36. To demonstrate the robustness of statistical techniques, top performing methods are compared with support vector regression. Furthermore, some potential future directions are discussed based on the results.