IMPLEMENTATION OF ARIMA MODELS ON DEMAND FORECASTING AT PT WORLD YAMATEX SPINNING MILLS
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Date
2009-12-10
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Universitas Widyatama
Abstract
Company activities are in uncertain circumtances so need for tool or method to predict or forecast the
future is deeply needed. This research, forecasting method, uses Autoregressive Integrated Moving
Average (ARIMA). ARIMA have ability to solve the forecasting problem by applying Autocorrelation And
Partial Autocorrelation Coefficients. Temporary model are (2,2,1)(1,1,1)15, (1,1,2)(1,1,1)15, and
(2,1,2)(1,1,1)15. Checking for Ljung-Box Q Statistics by Chi-Square (X2), one of the result are p-value at
lag 12 is 0.01, 0.039, and 0.024 for each model, because the closest value to 0.05, so the chosen
model is ARIMA(1,1,2)(1,1,1)15
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Keywords
Autoregressive Integrated Moving Average, Autocorrelation And Partial Autocorrelation Coefficients, Model Analysis