BibTex Citation Data :
@article{J.Gauss30385, author = {Tomi Ardi and Rukun Santoso and Alan Prahutama}, title = {IMPLEMENTASI SUBSET AUTOREGRESSIVE MENGGUNAKAN PAKET FITAR}, journal = {Jurnal Gaussian}, volume = {6}, number = {4}, year = {2017}, keywords = {Time Series, Time Series Non-stasioner, Subset AR, FitAR Package}, abstract = { Time series data analysis is one of the important points in statistics that is a time-dependent analysis. The commonly used model for time series data is ARIMA (Autoregressive Integrated Moving Average) or often also called the Box-Jenkins time series method. A model of ARIMA used in time clock data forecasting is the AR subset (autoregressive). The AR subset model is suitable for a long time series with a more than 5th order lag. The statistical software used is the R. time series AR subset approach on R using the FitAR package. The main function of the FitAR package is SelectModel and FitAR. SelectModel function to get the model automatically while FitAR is used to determine the temporary suspect model. Data used in the form of dataset contained in package FitAR that is SeriesA. The SeriesA data is data about the chemical concentration process observed every 2 hours for 17 days. SeriesA is processed using FitAR package so that the best model is AR [1,2,7]. Keywords : Time Series, Time Series Non-stasioner, Subset AR, FitAR Package }, issn = {2339-2541}, pages = {510--519} doi = {10.14710/j.gauss.6.4.510-519}, url = {https://ejournal3.undip.ac.id/index.php/gaussian/article/view/30385} }
Refworks Citation Data :
Time series data analysis is one of the important points in statistics that is a time-dependent analysis. The commonly used model for time series data is ARIMA (Autoregressive Integrated Moving Average) or often also called the Box-Jenkins time series method. A model of ARIMA used in time clock data forecasting is the AR subset (autoregressive). The AR subset model is suitable for a long time series with a more than 5th order lag. The statistical software used is the R. time series AR subset approach on R using the FitAR package. The main function of the FitAR package is SelectModel and FitAR. SelectModel function to get the model automatically while FitAR is used to determine the temporary suspect model. Data used in the form of dataset contained in package FitAR that is SeriesA. The SeriesA data is data about the chemical concentration process observed every 2 hours for 17 days. SeriesA is processed using FitAR package so that the best model is AR [1,2,7].
Keywords : Time Series, Time Series Non-stasioner, Subset AR, FitAR Package
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