Time series modeling and forecasting of the consumer price index Bandar Lampung

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: Kharimah, Faiga, Usman, Mustofa, Widiarti, Widiarti, Elfaki, Faiz Ahmed Mohamed
Format: Article
Language:English
Published: Sci.Int.(Lahore) 2015
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://irep.iium.edu.my/46113/
http://irep.iium.edu.my/46113/1/Faiga_SI_section_B_4619-.pdf
id iium-46113
recordtype eprints
spelling iium-461132017-03-08T01:21:21Z http://irep.iium.edu.my/46113/ Time series modeling and forecasting of the consumer price index Bandar Lampung Kharimah, Faiga Usman, Mustofa Widiarti, Widiarti Elfaki, Faiz Ahmed Mohamed QA75 Electronic computers. Computer science 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). Sci.Int.(Lahore) 2015-10 Article PeerReviewed application/pdf en http://irep.iium.edu.my/46113/1/Faiga_SI_section_B_4619-.pdf Kharimah, Faiga and Usman, Mustofa and Widiarti, Widiarti and Elfaki, Faiz Ahmed Mohamed (2015) Time series modeling and forecasting of the consumer price index Bandar Lampung. Science International Lahore, 27(5) (5). pp. 4619-4624. ISSN 1013-5316
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Kharimah, Faiga
Usman, Mustofa
Widiarti, Widiarti
Elfaki, Faiz Ahmed Mohamed
Time series modeling and forecasting of the consumer price index Bandar Lampung
description 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).
format Article
author Kharimah, Faiga
Usman, Mustofa
Widiarti, Widiarti
Elfaki, Faiz Ahmed Mohamed
author_facet Kharimah, Faiga
Usman, Mustofa
Widiarti, Widiarti
Elfaki, Faiz Ahmed Mohamed
author_sort Kharimah, Faiga
title Time series modeling and forecasting of the consumer price index Bandar Lampung
title_short Time series modeling and forecasting of the consumer price index Bandar Lampung
title_full Time series modeling and forecasting of the consumer price index Bandar Lampung
title_fullStr Time series modeling and forecasting of the consumer price index Bandar Lampung
title_full_unstemmed Time series modeling and forecasting of the consumer price index Bandar Lampung
title_sort time series modeling and forecasting of the consumer price index bandar lampung
publisher Sci.Int.(Lahore)
publishDate 2015
url http://irep.iium.edu.my/46113/
http://irep.iium.edu.my/46113/1/Faiga_SI_section_B_4619-.pdf
first_indexed 2023-09-18T21:05:39Z
last_indexed 2023-09-18T21:05:39Z
_version_ 1777410914765504512

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