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.
The national examination as one of the standard evaluation systems of education in Zimbabwe is used for the educational developments that seek to improve the quality of education in the educational sectors. This research aims to find the best model and its factors affecting the average pass rate of the Advanced Level (A-Level) national examination in Zimbabwe. Modelling was conducted using a two-level hierarchical model with factors influencing the national examination at district in the first level and those influencing the national examination provincial level in the second level. The Bayesian approaches namely hierarchical log-logistic and normal mixture were used in the modelling. The estimation of these Bayesian approaches posterior parameters was done using Markov Chain Monte Carlo (MCMC) and the Deviance Information Criterion (DIC) value was used to select the best model. The hierarchical normal mixture was found to be the best model to explain the variability of the average pass rate percentage of the A–Level national examination and all the micro and macro variables in this study significantly influenced the A-Level national examination in Zimbabwe.
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