Fitting a Time Series Model » książka
Fitting a Time Series Model
ISBN-13: 9783639170818 / Angielski / Miękka / 2009 / 96 str.
This study concentrates on fitting a time series model for paddy production in Sri Lanka. There are two main goals of time series analysis: (a) identifying the nature of the phenomenon represented by the sequence of observations, and(b)forecasting First, I plot the series and examine the main features of the graph, checking in particular if there is a trend, a seasonal component, any apparent sharp changes in behavior, or any outlying observations. Then remove the trend and seasonal component to get stationary residuals. To achieve this goal it may some times be necessary to apply a preliminary transformation to the data. A software package ITSM96 for windows is used to analysis the data, which was developed by P.J.Brockwell and R.A.Davis.To fit a time series model to paddy production data, first the non-stationary series was converted to a stationary series. The ACF and PACF graphs were used to find the order of the models. Based on minimum FPE, AICC, BIC and Maximum Likelihood statistic values, the several models such as MA, AR and ARMA models were fitted. Finally these models are used to forecast the paddy production up to year 2005.
This study concentrates on fitting a time series model for paddy production in Sri Lanka. There are two main goals of time series analysis:(a) identifying the nature of the phenomenon represented by the sequence of observations,and(b)forecasting First,I plot the series and examine the main features of the graph, checking in particular if there is a trend, a seasonal component, any apparent sharp changes in behavior, or any outlying observations. Then remove the trend and seasonal component to get stationary residuals. To achieve this goal it may some times be necessary to apply a preliminary transformation to the data. A software package ITSM96 for windows is used to analysis the data, which was developed by P.J.Brockwell and R.A.Davis.To fit a time series model to paddy production data, first the non-stationary series was converted to a stationary series. The ACF and PACF graphs were used to find the order of the models. Based on minimum FPE,AICC,BIC and Maximum Likelihood statistic values, the several models such as MA,AR and ARMA models were fitted. Finally these models are used to forecast the paddy production up to year 2005.
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