DSpace Repository

Modeling Persistent and Periodic Weekly Rainfall in an Environment of an Emerging Sri Lankan Economy

Show simple item record

dc.contributor.author Silva, H. P. T. N.
dc.contributor.author Dissanayake, G. S.
dc.contributor.author Peiris, T. S. G.
dc.date.accessioned 2022-09-27T09:27:00Z
dc.date.available 2022-09-27T09:27:00Z
dc.date.issued 2019
dc.identifier.citation Silva, H. P. T. N., Dissanayake, G. S., & Peiris, T. S. G. (2019). Modeling Persistent and Periodic Weekly Rainfall in an Environment of an Emerging Sri Lankan Economy. TES 2019, SCI 808, pp. 314–328, 2019. en_US
dc.identifier.uri http://dr.lib.sjp.ac.lk/handle/123456789/12436
dc.description.abstract The quantity of rainfall and its related events have become more and more uncertain due to climatic variability. The complexity of the rainfall pattern increases due to the changes of the atmospheric behavior from time to time. Relatively, few measures have been taken to perform the modeling of rainfall in the context of long memory. This paper provides an assessment of such a phenomenon by fitting an appropriate time series model. A long range dependency model is proposed to fit weekly rainfall data to explore characteristics of persistence through an unbounded spectral density. Careful examination of the data exhibits periodic fluctuations as an additional feature. Since, the rainfall series exhibits periodic variations and persistence, a seasonal autoregressive fractionally integrated moving average (SARFIMA) model is fitted. Parameters of it are estimated using maximum likelihood estimation (MLE) method. A Monte Carlo simulation was carried out with different seasonal and non seasonal fractionally differing parameters to measure the suitability of the method for parameter estimation. Best fitted model is chosen based on the minimum of the mean absolute error and the forecasting performance are compared with the result of Seasonal autoregressive integrated moving average (SARIMA) using an independent sample as a creative contribution. en_US
dc.language.iso en en_US
dc.publisher Springer Nature Switzerland en_US
dc.subject Seasonality · Rainfall · Fractional differencing Long-memory · Maximum likelihood estimators · Forecasting en_US
dc.title Modeling Persistent and Periodic Weekly Rainfall in an Environment of an Emerging Sri Lankan Economy en_US
dc.type Article en_US
dc.identifier.doi https://doi.org/10.1007/978-3-030-04263-9_24 en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Browse

My Account