-Electricity authorities in the UAE have not been successful in developing reliable and accurate models of system peak load. In this study, we develop a time-series-based decisionsupport system that integrates data management, model base management, simulation, graphic display, and statistical analysis to provide near-optimal forecasting models. The model base includes a variety of time-series ...

Most models for the time series of stock prices have centered on autoregressive (AR) processes. Traditionaly, fundamantal Box-Jenkins analysis [2] have been the mainstream methodology used to develop time series models. Next, we briefly describe the develop a classical AR model for stock price forecasting. A fuzzy regression model is then introduced. Following this description, an artificial fu...

Most models for the time series of stock prices have centered on autoregressive (AR) processes. Traditionally, fundamental Box-Jenkins analysis have been the mainstream methodology used to develop time series models. We briefly describe developing a classical AR model for stock price forecasting. Then a fuzzy regression model is introduced. Following this description, an artificial fuzzy neural...

The People’s Republic of China is one of the world’s most popular tourist destinations. This paper reviews the development of the Chinese inbound tourism industry after the cultural revolution and analyses tourist flows from Japan, which is the most important short-haul inbound market for China. Box-Jenkins univariate time series analysis facilitates an understanding of tourist arrival patterns...

Well-known Box-Jenkins Autoregressive integrated moving average (ARIMA) methodology has virtually dominated analysis of time-series data since 1930s. However, it is applicable to only those data that are either stationary or can be made so. Another limitation is that the resultant model is “Linear”. During the last two decades or so, the area of “Nonlinear time-series” is rapidly growing. Here,...

A genetic analysis of time series, i.e., long stretches of repeated observations such as typically encountered in psychophysiological research, raises problems that are related to the proper handling of autocorrelat ion. Fo r instance, a standard univariate technique such as A N O V A of repeated measures is based on the assumption of compound symmetry of the autocorrela t ion function. This me...

Recent studies have shown the classification and prediction power of the Neural Networks. It has been demonstrated that a NN can approximate any continuous function. Neural networks have been successfully used for forecasting of financial data series. The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and ou...

Recursive time series methods are very popular due to their numerical simplicity. Their theoretical background is usually based on Kalman filtering in state space models (mostly in dynamic linear systems). However, in time series practice one must face frequently to outlying values (outliers), which require applying special methods of robust statistics. In the paper a simple robustification of ...

Precipitation time series intrinsically contain important information concerning climate variability and change. Well-fit models of such time series can shed light upon past weather related phenomena and can help to explain future events. The objective of this study is to investigate the application of some conceptually different methods to construct models for large hydrological time series. W...

Associative Models were created and used for pattern recognition tasks, but recently such models have shown good forecasting capabilities; by a preprocessing of a time series and some fit of the Model. In this paper, the Gamma Classifier is used as a novel alternative for currency exchange rate forecasting, where experimental results indicate that the proposed method can be effective in the Exc...