Time series modeling and forecasting of the consumer price index Bandar Lampung
The aims of this study are to find the best Time Series model for forecasting the Consumer Price Index (CPI). To find the best model, first we evaluate the stationary of the data by using time series plot, Autocorrelation Function (ACF), and Unit root Test. Then the Time Series model was found by us...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sci.Int.(Lahore)
2015
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/46113/1/Faiga_SI_section_B_4619-.pdf http://irep.iium.edu.my/46113/ |
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| Institution: | Universiti Islam Antarabangsa Malaysia |
| Language: | English |
| Summary: | The aims of this study are to find the best Time Series model for forecasting the Consumer Price Index (CPI). To find the best model, first we evaluate the stationary of the data by using time series plot, Autocorrelation Function (ACF), and Unit root Test. Then the Time Series model was found by using ACF and Partial Autocorrelations Function (PACF). The best model was found by using the criteria: Mean Squares Error (MSE), Akaike Information Criteria (AIC) and Bayesian Information Criterion (BIC. Based on this criteria the best modelfound in this paper is ARIMA (1,1,0) compare to ARIMA (0,1,1), and ARIMA(1,1,1). |
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