This paper presents methods for analyzing and manipulating unevenly spaced time series without a transformation to equally spaced data. Processing and analyzing such data in its unaltered form avoids the biases and information loss caused by resampling. Care is taken to develop a framework consistent with a traditional analysis of equally spaced data, as in Brockwell and Davis (1991), Hamilton ...

In this paper a new fuzzy system modeling algorithm is introduced as a data analysis and approximate reasoning tool. The performance of the proposed algorithm is tested in two different data sets and compared with some well-known algorithms from the literature. In the comparison two benchmark data sets from the literature, namely the automobile mpg (miles per gallon) prediction and Box and Jenk...

We discuss the classic auto regressive statistical models Box and Jenkins used in prediction problems, and their application is analyzed in the energy sector. Subsequently, we propose a methodology based on the application of these models for the prediction of electric power generation, from data obtained at a wind farm in Ventosa, Oaxaca. Finally, the model and the preliminary results obtained...

The paper is devoted to the presentation of methods of economic time series analysis and modelling using the Box-Jenkins methodology, the signal processing approach and the feedforward conventional/fuzzy neural network technique. Some results on our research on time series modelling with emphasis on potential improving forecast accuracy are presented here. The assessment of the particular model...

The procedure of reducing noise or reduction before analyzing the time series is important to get accurate and reliable outcomes when building models. Wavelet Shrinkage consisting wavelets with thresholding a powerful mathematical method used reduce that can be exposed. It has time-series observations selects cut-off threshold level suitable for removing most noise. In this paper, natural gas p...

Prediction has been widely studied for many years as time series analysis (Box & Jenkins, 1970; Tong, 1990). Traditionally, prediction is based on a statistical model that is either linear or nonlinear (Li et al., 1990). Recently, several studies have adopted neural fuzzy networks to predict time series (Cowder, 1990; Kasabov & Song, 2002; Ling et al., 2003). Researchers have discussed that the...

The paper presents a genetic fuzzy rule-based technique for the modelling of generalized time series (containing both, numerical and non-numerical, qualitative data) which are a comprehensive source of information concerning the behaviour of many complex systems and processes. The application of the proposed approach to the fuzzy rule-based modelling of an industrial gas furnace system using me...