نتایج جستجو برای: auto regressive moving average time series
تعداد نتایج: 2475685 فیلتر نتایج به سال:
Great Salt Lake (GSL) is the largest salt lake in the western hemisphere, the fourthlargest terminal lake in the world. The elevation of GSL has critical effect on the people who live nearby and their properties. It is crucial to build an exact model of GSL elevation time series in order to predict the GSL elevation precisely. Although some models, such as ARIMA or FARIMA (fractional auto-regre...
In this paper, for the first time, the subject of change point estimation has been utilized in the stationary state of auto regressive moving average (ARMA) (1, 1). In the monitoring phase, in case the features of the question pursue a time series, i.e., ARMA(1,1), on the basis of the maximum likelihood technique, an approach will be developed for the estimation of the stationary state’s change...
In this study we have analysed wind and wave time series data resulting from hourly measurements on the sea surface in Bushehr, the northern part of the Persian Gulf, from 15 July to 4 August 2000. Wind speed (U10) ranged from 0.34 to 10.38 m/s as alternating sea and land breezes. The lowest wind speed occurs at about midnight and the highest at around noon. The calculated autocorrelation of wi...
Forecasting of sea level fluctuations is a suitable tool for comprehensive management of the sea and the protection of coastal areas. On the other hand, application of time series analysis for forecasting purposes has been evaluated to be very appropriate. Therefore, two time series consisting monthly measured sea level data were used in the present research. The data have been recorded at two ...
Forecasting models have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecasters in such unstable environments. Forecasting methods such as Auto Regressive Integrated Moving Average (ARIMA) models and also Artificial Neural Networks (ANNs) need ...
Forecasting models have wide applications in decision making. In the real world, rapid changes normally take place in different areas, specifically in financial markets. Collecting the required data is a main problem for forecasters in such unstable environments. Forecasting methods such as Auto Regressive Integrated Moving Average (ARIMA) models and also Artificial Neural Networks (ANNs) need ...
The impact of fast moving items, modeled by auto-regressive moving average (ARMA) type processes, on the bullwhip effect is well known. However, slow moving items that can be modeled using integer ARMA processes have received little attention. Herein, we measure the impact of bullwhip effect under a first order integer auto-regressive, INAR(1), demand process. We consider a simple two-stage sup...
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