Identification of Time Series Model: An Application Part - Wan Muhamad Amir Bin W Ahmad; Norhayati Rosli; Norizan Mohamed; Zalila Binti Ali (STATISTIKA: Forum Teori dan Aplikasi Statistika, 2006)
Time series analysis generally referred to any analysis which involved to a time series data. In thisanalysis, any of the continuous observation is commonly dependent. If the continuous observation isdependable, then the values that will come are able to be forecasted from the previous observation(Weir 2006). If the behaviour of coming time series are able to be exactly forecasted based on previoustimes series, so it’s called deterministic time series. The objective of times series can be summarizedas to find the statistical model to describe the behaviour of the time series data and afterwards madeuse of skilled statistical techniques for estimation, forecasting but also the controlling. The use oftime series analysis very much spread in various fields like biology, medical and many more that hada purpose for forecasting. In this paper the recognition of concerning the Autoregressive Processmodel AR (p), Moving Average Process MA (q), Autoregressive Moving Average ARMA (p,q),Autoregressive Integrated Moving Average ARIMA (p,d,q) was given attention through the approach tothe Autocorrelation Function ACF and Partial Autocorrelation Function (PACF) theory plot.