Open AccessForecasting performance comparison of daily maximum temperature using ARMA based methods
Author(s) -
J Asha,
Santhosh Kumar S,
S Rishidas
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1921/1/012041
Subject(s) - autoregressive fractionally integrated moving average , autoregressive integrated moving average , moving average , autoregressive model , mean squared error , autoregressive–moving average model , statistics , mean absolute error , mathematics , econometrics , mean absolute percentage error , series (stratigraphy) , time series , long memory , volatility (finance) , paleontology , biology
Daily maximum temperature of four different regions in Kerala, India, from 01/01/2019 to 31/12/2020, is recorded and is used for modelling and forecasting. The forecasting methods used are Autoregressive integrated moving average (ARIMA), Seasonal Autoregressive integrated moving average (SARIMA) and Autoregressive fractional integrated moving average (ARFIMA). The comparison of forecasting performance was based on percentage accuracy, mean squared error (MSE) and mean absolute error (MAE). The models used can capture the variations of time series data. All the models exhibit reasonably good performance in predicting the daily maximum temperature. ARFIMA model gives the least forecast errors compared to other models.