comparison of linear and nonlinear (bilinear) time series models in reference crop evapotranspiration prediction in urmia synoptic stationreference crop evapotranspiration (eto) prediction is one of the important elements in optimizing agricultural water consumption. in this regard, one of the prediction approaches is to use the stochastic time series methods. in this research, ar (p) and arma(...

In this paper we have presented an IntegratedWavelet Neural Network (WNN) model and Mutual Information (MI)-based input selection algorithm for time series prediction. Based on MI the proper input variables, which describe the time series’ dynamics properly, will be selected. The WNN Prediction model uses selected variables and predicts the future. This model utilized for time series prediction...

This research was supported by the Army, Navy, Air Force and NASA under a contract. administered by the Office of Naval Research, with Yale University; by the National Science Foundation under Grant GU-2059 and the Air Force Office of Scientific Research under Contract AFOSR-68-l4l5 with the University of North Carolina at Chapel Hill. ** The major portion of the research for this paper was don...

We propose a new method to predict time series using the technique of Independent Component Analysis (ICA) as a preprocessing tool. If certain assumptions hold, we show that ICA can be used to transform a set of time series into another set that is easier to predict. These assumptions are not unrealistic for many real-world time series, including financial time series. We have tested this appro...

In this article, a new combined approach of a decision tree and clustering is presented to predict the transmission of genetic diseases. In this article, the performance of these algorithms is compared for more accurate prediction of disease transmission under the same condition and based on a series of measures like the positive predictive value, negative predictive value, accuracy, sensitivit...

In this paper, we consider the temporal pattern in traffic flow time series, and implement a deep learning model for traffic flow prediction. Detrending based methods decompose original flow series into trend and residual series, in which trend describes the fixed temporal pattern in traffic flow and residual series is used for prediction. Inspired by the detrending method, we propose DeepTrend...

in this paper modeling and forecasting of revenue of taxes in fifth development plan is investigated based on a special structure of nonlinear neural networks. the time series of taxes which are studied in this research are related to total tax, direct tax, indirect tax, companies’ tax, income tax, wealth tax, and import tax. based on the correlation dimension estimation technique, the structur...

analyses and investigations on river flow behavior are major issues in design, operation and studies related to water engineering. thus, recently the application of chaos theory and new techniques, such as chaos theory, has been considered in hydrology and water resources due to relevant innovations and ability. this paper compares the performance of chaos theory with anfis model and discusses ...