Assessing Forecasting Performance of Daily Mean Temperature at 1st and 2nd Perak Station, Surabaya Using ARIMA and VARIMA Model with Outlier Detection

Abstract

Air temperature is an important data for several sectors. The demand of fast, exact and accurate forecast on temperature data is getting extremely important since it is useful for planning of several important sectors. In order to forecast mean daily temperature data at 1st and 2nd Perak BMKG Station in Surabaya, this study used the univariate method, ARIMA model and multivariate method, VARIMA model with outlier detection. The best ARIMA model was selected using in-sample criteria, i.e. AIC and BIC. While for VAR model, the minimum information criterion namely AICc value was considered. The RMSE values of several forecasting horizons of out-sample data showed that the overall best model for mean daily temperature at 1st and 2nd Perak Station was the multivariate model, i.e. VARX (10,1) with four outliers incorporated in the model, indicated that it was necessary to consider the temperature from the nearest stations to improve the forecasting performance. This study recommends performing the overall best model only for short term forecasting, i.e. two weeks at maximum. By using the one week-step ahead and one day-step ahead forecasting scheme, the forecasting performance is significantly improved compared to default the k-step ahead forecasting scheme.

Integrating Estimation of Inflation Threshold and Nowcasting Regional Economic Growth Using Classical, Machine Learning, and Ensemble Method

“…Untuk menjawab pertanyaan ini, selanjutnya akan dilakukan perbandingan performa prediksi dari model pada penelitian ini dengan prediksi dari model benchmark, yakni prediksi menggunakan rata-rata dari data in-sample (mean based prediction). Model benchmark ini adalah model paling sederhana yang dapat digunakan sebagai pembanding dari model prediksi dari suatu penelitian (Purwa & Ngwarati, 2022). Model penelitian dianggap sudah tidak baik digunakan untuk prediksi pertumbuhan ekonomi regional jika selisih antara RMSE model benchmark dengan model penelitian (RMSE reduction) bernilai negatif atau RMSE model benchmark < RMSE model penelitian.…”