Time Series Model for Predicting Ground-Level Ozone
Author(s) : KUNLAWEE MANWONG, PASAPITCH CHUJAI
Abstract
This research is a comparison for forecasting methods for ground-level ozone using ARIMA (Auto-Regressive Integrated Moving Average) and GARCH (Generalized Autoregressive Conditionally Heteroskedastic) for the forecasting of four places in Thailand, from January 2013 to August 2018. The results obtained compare between the two models above mentioned in order to find the most accurate one, considering the lowest RMSE (Root Mean Square Error) and the lowest MAPE (Mean Absolute Percentage Error). According to the experiment, the most suitable method is GARCH which is good for the 1-2 hours early forecasting.
No fo Author(s) : 2
Page(s) : 16-21
Electronic ISSN : 2319 - 7498
Volume 8 :
Issue 1
Full Paper PDF
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