Identification of Time Series Model: An Application Part

Abstract

Time series analysis generally referred to any analysis which involved to a time series data. In this analysis, any of the continuous observation is commonly dependent. If the continuous observation is dependable, 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 previous times series, so it’s called deterministic time series. The objective of times series can be summarized as to find the statistical model to describe the behaviour of the time series data and afterwards made use of skilled statistical techniques for estimation, forecasting but also the controlling. The use of time series analysis very much spread in various fields like biology, medical and many more that had a purpose for forecasting. In this paper the recognition of concerning the Autoregressive Process model 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 to the Autocorrelation Function ACF and Partial Autocorrelation Function (PACF) theory plot.