Abstract.A short memory process that encounters occasional structural breaks in mean can show a slower rate of decay in the autocorrelation function and other properties of fractional integrated I (d) processes. In this paper we employed a procedure for estimating the fractional differencing parameter in semiparametric contexts proposed by Geweke and Porter-Hudak (1983) to analyse nine daily rainfall data sets across Malaysia. The results indicate that all the data sets exhibit long memory. Furthermore, an empirical fluctuation process using the ordinary least square (OLS)-based cumulative sum (CUSUM) test for the break date was applied. Break dates were detected in all data sets. The data sets were partitioned according to their respective break date, and a further test for long memory was applied for all subseries. Results show that all subseries follows the same pattern as the original series. The estimate of the fractional parameters d 1 and d 2 on the subseries obtained by splitting the original series at the break date confirms that there is a long memory in the data generating process (DGP). Therefore this evidence shows a true long memory not due to structural break.

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Fish catch prediction is an important problem in the fisheries sector and has a long history of research. The main goal of this paper is to create a model and make predictions using fish catch data of two fish species. Among the most effective and prominent approaches for analyzing time series data is the methods introduced by Box and Jenkins. In this study we applied the Box-Jenkins methodology to build Seasonal Autoregressive Integrated Moving Average (SARIMA) model for monthly catches of two fish species for a period of five years (2007 -2011). The seasonal ARIMA (1, 1, 0)(0, 0, 1) 12 and SARIMA (0, 1, 1) (0, 0, 1) 12 models were found fit and confirmed by the Ljung-Box test and these models were used to forecast 5 months upcoming catches of Trichiurus lepturus (Ikan Selayor) and Amblygaster leiogaster (Tambun Beluru) fish species. The result will help decision makers to establish priorities in terms of fisheries management.

Hazardous situations related to rainfall events can be due to very intense rainfall, or to the persistence of rainfall over a long period of time. Such events may result to an exceedence of the capacity of drainage systems resulting in the heap of basements which may lead to landslides and flooding. This study assesses the persistence dependence of rainfall time series of Chui Chak, a station in Peninsular Malaysia that observed the highest rainfall event for the period 01/01/1975-31/12/2008. The persistence dependence of the rainfall time series was modelled via fractional ARIMA model augmented with the GARCH model. The Ljung-Box test for testing autocorrelation proves that the combined ARFIMA-GARCH model captures the temporal persistence behaviour in the Chui Chak rainfall time series data with persistence measure 0.839. This measure represents a relatively lasting persistence, that is, the process variability should return to the historical average after a relatively long period of time which may have a risk of extreme event.

Abstract. Wind speed is a fundamental atmospheric variable which plays an important role in energy production industry. Wind speed affects weather forecasting, aircraft and maritime operations, and other numerous effects. As a rising tenders of wind energy, it is vital for power efficacies to plan the adaptation of wind power. Henceforth, an accurate measurement of wind speed prediction is ideal for providing an impression on how the behavior and trend of historical wind pattern and future projected pattern could be. Wind speed forecasting is important for the reliable and efficient operation of the wind power system. The ability of Fourier based-ARMA model was tested to forecast the wind speed data of 3 crucial locations; Senai, Bayan Lepas and Subang. A comparison has been made between conventional method and Fourier analysis method. Fourier-ARMA was found to outperform the conventional ARMA model in predicting the wind speed data for 365 days ahead with just a little error.