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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.
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.