Abstract:
Awareness of pattern of weekly rainfall and its variability facilitate to make effective
decisions with respect to climate monitoring. Though various statistical and non statistical
techniques have been developed for rainfall modeling with increasing degree of accuracy,
there is still a noticeable gap for prediction of rainfall. The aim of this study was to model
weekly rainfall in context of long memory along with the conditional heteroskedasticity.
Weekly rainfall data (1990-2017) in Colombo city was obtained from the Department of
Meteorology, Sri Lanka. Of the various types of long memory models developed for
weekly series, the best fitted model is ARFIMA-GARCH for deseasonalized data. The
model was trained using weekly rainfall data from 1990 to 2014 and validated using
weekly data from 2015 to 2017. The forecasting performance of the new model is not
much diluted with the increase of the forecasting length. The exact maximum likelihood
estimation method was utilized to estimate the model parameters, and Monte Carlo
simulation was carried out with various fractional differencing parameters to evaluate the
suitability of the estimation method. The simulation study provided the empirical evidence
to optimal accuracy of parameter estimation. The best fitted model developed is ARFIMAGARCH
for deseasonalized data. The forecasting performance of the model was evaluated
based on the novel index developed using absolute error for an independent data set in
addition to the classical indicators. The novel long range dependency model is
recommended to be used in forecasting weekly rainfall in Colombo city in Sri Lanka.
