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The statistical models required the large data in the time series forecasting. While, to forecast the limited data or small data cannot be suggested by using these models. In this paper, we are interested to apply fuzzy random auto-regression model to handle the university enrollment data. The accuracy of the forecasting model can be improved through the left-right procedure. The yearly enrollment...
Many models have been suggested to predict the weather and temperature data. Most of them used the single point data in building prediction equations. Besides that, the randomness, the vagueness and possibility of the temperature data are also not much concerned. In this paper, we introduce the minimum-maximum procedure for daily temperature modeling based on fuzzy random auto-regression time series...
Forecasting Multiple Time Series (MTS) consists of multiple time series with no relation between them and independent of each other. Predicting each time series independently may lead to increase in time and cost. In this paper, we formalize the problem of predicting the multiple time series together over a MTS database. The proposed framework addresses the following issues. First, it build the initial...
As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation...
According to the noise in the nonlinear systems and shortage of chaotic prediction method at present, this paper presents a local linear adaptive prediction algorithm based on the kernel function of wavelet decomposition. This method using wavelet transformation has a unique multi-scale analysis capability, decomposed the singular into low frequency part and high frequency part, thereby it can reduce...
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