hydrologists have always tried to classify atmospheric and hydrologic events in order to simplify the hydrologic convolutions and the observations or to save the time and the budget. most of these methods are used for the regionalization of hydrologic phenomena like rainfall,streamflow and other components of water cycle.multivariate techniques have been underlined as suitableand powerful tools...

In this study, a vector autoregression (VAR) model with time-varying parameters (TVP) to predict the daily Indian rupee (INR)/US dollar (USD) exchange rates for the Indian economy is developed. The method is based on characterization of the TVP as an optimal control problem. The methodology is a blend of the flexible least squares and Kalman filter techniques. The out-of-sample forecasting perf...

This paper considers univariate online electricity demand forecasting for lead times from a half-hour-ahead to a day-ahead. A time series of demand recorded at half-hourly intervals contains more than one seasonal pattern. A within-day seasonal cycle is apparent from the similarity of the demand profile from one day to the next, and a within-week seasonal cycle is evident when one compares the ...

Abstract: This paper proposed a new method to estimate the missing data by using the filtering process. We used datasets without missing data and randomly missing data to evaluate the new method of estimation by using the Box Jenkins modeling technique to predict monthly average rainfall for site 5504035 Lahar Ikan Mati at Kepala Batas, P. Pinang station in Malaysia. The rainfall data was colle...

Risks have recently emerged as a key issue for the life cycle of a supply chain. Operations risks are particularly important because of the propagation of their effects. An ARIMA model of a supply chain is proposed to represent flow dynamics in a network of enterprises and their responses to strong disturbances from the demand and/or the offer side. When the amplitude of product flows variation...

Worldwide, influenza is estimated to result in approximately 3 to 5 million annual cases of severe illness and approximately 250,000 to 500,000 deaths. We need an accurate time-series model to predict the number of influenza patients. Although time-series models with different time lags as feature spaces could lead to varied accuracy, past studies simply adopted a time lag in their models witho...