Time Series Analysis of Price of Coffee in Case of Mettu Town, Ilu Ababor Zone, Oromia Regional State, Ethiopia

Background: Coffee is one of the most important cash crops across the world and major source of export earnings. Coffee has been and remains the leading cash crop and export commodity of Ethiopia. The aim of this study was to estimate and predict the price change of coffee in Mettu town. Methods: In this study both descriptive and inferential statistics were used to analyze secondary data that were collected from Mettu town of Ethiopian Commodity Exchange office sector. A total of 120 months of price of coffee was included in this study. Time series analysis was used to estimate the parameter and for forecast the future values of price change of coffee. Result: The original data was not stationary and become stationary after second order differencing. The results showed that the price change of coffee was increasing from time to time. After that the data tested the order MA and AR are identified by using the ACF and PACF. Then the model was selected by using AIC. Since, ARMA (1, 2, 1) for price change of coffee was lower values of AIC found to be the most appropriate model to fit the data of the price change of coffee. After the model was fitted, the diagnostic checking has been applied by using the ACF residual and normality checking. So that the model fitted is appropriate for the price change of coffee. All the forecast values are found between the lower and the upper interval then we can say that forecasted value is accurate.

Modelling extreme precipitation: an application to two selected rainfall stations in Malaysia

Statistical models of rainfall have been applied in the understanding of the rainfall past trends, identifying for any anomalies, and making projections of future climate change in Malaysia. Herein, we analyse the rainfall data of 7-year period using the gamma and beta regression models to fit Malaysian extreme precipitation events of two stations, each in the West Coast region and the East Coast region, with extreme precipitation calendar date (in the angular form) as the predictor of the models. While the significance test as the p-value is much less than 0.05, it shows that there is a significant relationship between the climatology response variables. The deviance residual plot will be used to check the goodness of fit for diagnostic checking. The results show the models are useful in highlighting the latest trends and projections of climate change in Malaysia.

Fast leave-one-out methods for inference, model selection, and diagnostic checking

In this article, we describe jackknife2, a new prefix command for jackknifing linear estimators. It takes full advantage of the available leave-one-out formula, thereby allowing for substantial reduction in computing time. Of special note is that jackknife2 allows the user to compute cross-validation and diagnostic measures that are currently not available after ivregress 2sls, xtreg, and xtivregress.

Vector Autoregressive Integrated (VARI) Method for Forecasting the Number of Internasional Visitor in Batam and Jakarta

Forecasting methods that are often used are time series analysis, the Autoregressive (AR) method. The AR method only carries out univariate analysis, meaning that it carries out a separate model between the number of international visitor coming to Indonesia through Batam and Jakarta. Though there is a possibility, the number of international visitor arriving through Jakarta affects the number of international visitor arriving through Batam. Therefore, in this study the Vector Autoregressive Integrated (VARI) method is used. The VARI model is used on the number of international visitor arrivals per month at Batam and Jakarta for the period Januari 2014 – December 2019. VARI model formation through several stages, namely stationarity test, autoregressive order determination, VARI model formation, and diagnostic checking of the model. With the VARI model, VARI(5,1), the two significant simultaneously equation results are obtained. The Mean Absolute Percentage Error (MAPE) in this model are as follows 1,98% and 2,48% in predicting the number of international visitor arrivals in Batam and Jakarta. In this study also forecasting the number of international visitor arrivals in Batam and Jakarta in January – December 2020

SARIMA Modelling and Forecasting of Monthly Rainfall Patterns for Coimbatore, Tamil Nadu, India

Weather forecasting is an important subject in the field of meteorology all over the world. The pattern and amount of rainfall are the essential factors that affect agricultural systems. The present paper describes an empirical study for modeling and forecasting the time series of monthly rainfall patterns for Coimbatore, Tamil Nadu. The Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) methodology has been adopted for model identification, diagnostic checking and forecasting for this region. The best SARIMA models were selected based on the Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) and the minimum values of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The study has shown that the SARIMA (0,0,0)(2,0,0)12 model was appropriate for analysing and forecasting the future rainfall patterns. The Root Means Square Error (RMSE) values were found to be 52.37 and proved that the above model was the best model for further forecasting the rainfall.

Aplikasi Model Vector Autoregressive (VAR) pada Data Tamu Mancanegara di Hotel Bintang dan Non Bintang di Daerah Istimewa Yogyakarta

Model Vector Autoregressive (VAR) merupakan gabungan dari beberapa model Autoregressive (AR), dimana model membentuk sebuah vektor yang antara variabel-variabelnya saling memengaruhi. Model AR(1) menyatakan bahwa pengamatan waktu sekarang dipengaruhi pengamatan satu waktu sebelumnya dan unsur error. Pada analisis ini, model Vector Autoregressive (VAR) digunakan pada data tamu mancanegara per bulan yang menginap di Hotel Bintang dan Non bintang di Daerah Istimewa Yogyakarta per bulan periode Januari 2008 sampai dengan Desember 2015. Pembentukan model VAR melalui beberapa tahap yaitu: uji stasioneritas, penentuan orde autoregressive, pembentukan model VAR, dan diagnostic checking. Untuk pengolahan data dilakukan dengan program R 3.5.1. Dari analisis data, variabel jumlah tamu wisatawan mancanegara di Hotel Bintang dan Hotel Non Bintang di Daerah Istimewa Yogyakarta memiliki korelasi yang cukup tinggi yaitu sebesar 0,91. Dengan model Vector Autoregressive (VAR) yaitu VAR(1) didapatkan kedua hasil persamaan simultan yang signifikan. Nilai R2 dan Adjusted R2 kedua persamaan parsial model VAR(1) cukup tinggi yaitu untuk persamaan variabel Hotel Bintang didapatkan R2 sebesar 71,13% dan Adjusted R2 70,5%, sedangkan untuk persamaan variabel Hotel Non Bintang didapatkan R2 sebesar 76,56% dan dan Adjusted R2 70,65 %.

GENERALIZED SPACE-TIME AUTOREGRESSIVE (GS-TAR) (Studi Kasus Peramalan Harga Saham Syariah Empat Perusahaan di JII)

Abstrak: Data deret waktu dari beberapa lokasi yang berdekatan seringkali mempunyai hubungan yang saling bergantung. Data yang tidak hanya mempunyai keterkaitan dengan kejadian pada waktu-waktu sebelumnya, tetapi juga mempunyai keterkaitan dengan lokasi lain disebut dengan data space-time. Model Generalized Space-Time Autoregrresive (GS-TAR) adalah suatu model yang banyak digunakan untuk memodelkan dan meramalkan data deret waktu dan lokasi. Adapun tujuan dari penelitian ini yaitu untuk mengaplikasikan model GS-TAR pada studi kasus memodelkan empat perusahaan yang termasuk saham syariah Jakarta Islamic Index (JII). Penelitian ini membahas tentang langkah-langkah analisis data runtun waktu dengan model GS-TAR. Metode ini terdiri dari beberapa tahap, yaitu uji stasioneritas, identifikasi model, estimasi parameter, diagnostic checking dan peramalan. Model runtun waktu GS-TAR (1;1) dapat melakukan peramalan harga saham syariah dengan baik. Hasil peramalan empat perusahaan yang diperoleh menunjukkan bahwa data dari hasil peramalan mendekati data aktual. Kata kunci : Model GS-TAR, Peramalan, Saham Syariah, Space-Time