COMPARATIVE ANALYSIS OF FORECASTING MODELS OF THE AIR CONDITION INDICATOR

Periodic time series have many applications in our lives. Examples of periodic time series are air quality indicators, financial market indicators, meteorological parameters, etc. Because of this, the analysis and forecasting of periodic time series is a widespread and interesting scientific topic. One of the main problems in the analysis of periodic time series is the determination of the parameters of the seasonality of this series and the identification and elimination of abnormal values that can significantly affect the accuracy of data forecasting. In this work, a comparative analysis of the previously developed models and approaches for air quality indicator forecasting is given. The research is based on real data from the EcoCity public air quality monitoring network. A brief description of the method of identifying the seasonality parameters of the time series based on the decomposition of this time series and an approach to finding local anomalies in a time series based on the results of series decomposition is given. The results of the described models were used to forecast the PM2.5 dust index of one of the air quality monitoring stations in the Vinnytsia region. The Python programming language was used to automate the forecasting process, and the program code itself was implemented in the Kaggle system, a web platform from Google for machine learning engineers. The Prophet time series model was used for forecasting. A comparison table of the forecast accuracy of the Prophet model with default settings and with custom configuration based on the data from developed models and approaches was provided. The study and analysis showed that using both developed methods helps to reduce the forecasting error for the air quality indicator. Compared to the accuracy of the Prophet model with the default parameters, it was possible to reduce the MAE error value by 30% and the RMSE by 21%. This proves that these methods are effective for the analysis and forecasting of time series, including time series of air quality indicators.