Forecasting the Monthly Average of Particulate Matter 2.5 in Chiang Mai Province using the Classical Decomposition Method
DOI:
https://doi.org/10.57260/stc.2024.800Keywords:
Forecasting, Particulate matter 2.5, Classical decomposition method, Chaing MaiAbstract
Time series forecasting using the classical decomposition method can be predicted 12 months ahead, this method was chosen to forecast the monthly average of particulate matter no larger than 2.5 microns (PM2.5) in Chiang Mai Province. The data was collected from a permanent measuring station in Mueang District, Chiang Mai Province, City Hall, Mueang District (35T) for the years 2020, 2021, 2022, and January to June 2023. The four decomposition methods—the additive model and the three multiplicative models—the simple average method, ratio to trend, and centered moving average—were applied to PM2.5 data. The results showed that when using the monthly average data for the years 2020, 2021, and 2022 to develop the model, the accuracy of the model when compared with 36-month observational values, the additive model has the lowest mean absolute percentage error (MAPE) of 7.06, which is within the criteria for highly precise forecasting. But when the monthly forecast values for the year 2023 are compared with the observation values from January to June 2023, there have already been abnormally high monthly average events in February, March, and April, the multiplicative model with the simple average method had the lowest MAPE at 28.72. The forecast values for July–December will have a monthly average PM2.5 between 17 and 26 micrograms per cubic meter.
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