Abstract:
A study of rainfall pattern and its variability in South Asian
countries is vital as those regions are frequently vulnerable to climate
change. Models for rainfall have been developed with different degrees
of accuracy, since this key climatic variable is of importance at local and
global level. This study investigates the rainfall behaviour using the long
memory approach. Since the observed series consists of an unbounded
spectral density at zero frequency, a fractionally integrated auto regressive
model (ARFIMA) is fitted to explore the pattern and characteristics
of the weekly rainfall in the city of Colombo. The maximum likelihood
estimation (MLE) method was utilized to obtain estimates for model
parameters. To evaluate the suitability of the method for parameter
estimation, a Monte Carlo simulation was done with various fractionally
differenced parameter values. Model selection was done based on
the minimum of the mean absolute error and validated by the forecasting
performance that was evaluated using an independent sample. The
experimental result yielded a good prediction accuracy with a best fitted
long range dependency model and a coverage probability of 95% in terms
of prediction intervals that resulted in closer nominal coverage.
