Industrial applications of short-term prediction on chaotic time series by local fuzzy reconstruction method

Optimization models based on GM (1,1) and seasonal fluctuation for electricity demand forecasting

“…Researchers believe that electricity demand seems chaotic, many complicated facts such as temperature, price of electricity and many other factors [3][4][5][6][7][8][9] can influence electricity demand. With the power systems growth and the increase in their complexity, many factors have become influential in electric power generation and consumption.…”

Residual Analysis and Combination of Embedding Theorem and Artificial Intelligence in Chaotic Time Series Forecasting

“…where r, r, and b are dimensionless parameters and the typical values for these parameters are r ¼ 10, r ¼ 28, and b ¼ 8=3 (Iokibe et al 1997;Jang, Sun, and Mizutani 1997). The x-coordinate of the Lorenz time series is considered for prediction, and a time series with a length of 1000 is generated as described in Rojas et al (2008).…”

Hybridization of intelligent techniques and ARIMA models for time series prediction

“…the root mean square error and the correlation coefficient, for this simulation were 0.176 and 0.998 for the model using only ANNs and 0.0876 and 0.9996 for the proposed algorithm. It is important to note other approaches appeared in the literature; for example Iokibe et al [27] obtain an RMSE of 0.244, Jang et al [28] an RMSE of 0.143, using fuzzy and neuro-fuzzy systems. Fig.…”

“…Two of the forecasting techniques that allow for the detection and modelling of nonlinear data are rule induction [46,27,21,44] and neural networks [57,47,51]. Rule induction identifies patterns in the data and expresses them as rules.…”