Online fuzzy time series analysis based on entropy discretization and a Fast Fourier Transform

“…We study these data from 1999 to 2004 to compare CFTS with other FTS algorithms. We use data vectors of November and December of each of the data sets for testing and the rest for training CFTS as in [30]. Two FTS A c c e p t e d M a n u s c r i p t 13 algorithms are presented in [30].…”

Section: Comparison Of Cfts With Recent Fts Algorithmsmentioning

confidence: 99%

A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables

Askari

Montazerin

Zarandi

2015

Applied Soft Computing

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“…Forecasting using FTS is applied to several areas, including forecasting university enrollments [3], sales, road accidents and financial forecasting [7,8,12,53]. In a conventional time series, the recorded values of a special dynamic process are represented by crisp numerical values.…”

Section: Introductionmentioning

confidence: 99%

Rainfall and financial forecasting using fuzzy time series and neural networks based model

Singh¹

2016

Int. J. Mach. Learn. & Cyber.

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In this study, the author presents a new model to deal with four major issues of fuzzy time series (FTS) forecasting, viz., determination of effective lengths of intervals (i.e., intervals which are used to fuzzify the numerical values), repeated fuzzy sets, trend associated with fuzzy sets, and defuzzification operation. To resolve the problem of determination of length of intervals, this study suggests the application of an artificial neural network (ANN) based algorithm. After generating the intervals, the historical time series data set is fuzzified based on FTS theory. In part of existing FTS models introduced in the literature, each fuzzy set is given equal importance, which is not effective to solve real time problems. Therefore, in this model, it is recommended to assign weights on the fuzzy sets based on their frequency of occurrences. In the FTS modeling approach, fuzzified time series values are further used to establish the fuzzy logical relations (FLRs). To determine the trends associated with the fuzzy sets in the corresponding FLR, this article also introduces three trend-based conditions. To deal repeated fuzzy sets and trend associated with them, this study proposes a new defuzzification technique. The proposed model is verified and validated with real-world time series data sets. Empirical analyzes signify that the proposed model has the robustness to deal one-factor time series data sets very efficiently than existing FTS models. Experimental results show that the proposed model also outperforms over the conventional statistical models.

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“…Recently, some heuristic optimization algorithms have been used to get better results by Kuo et al [65,66], Davari, et al [43], Park et al [77] and Hsu et al [56], Chen and Chung [29], Lee et al [69,70]. Chen [20] and Chen and Chen [21] preferred to use entropy-based partitioning approach to get intervals in his high-order forecasting model. Wang et al [90] took the temporal information into account to partition the universe of discourse.…”

Section: Literature Reviewmentioning

confidence: 99%

A combined robust fuzzy time series method for prediction of time series

Yolcu

Lam

2017

Neurocomputing

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In case of outlier(s) it is inevitable that the performance of the fuzzy time series prediction methods is influenced adversely. Therefore, current prediction methods will not be able to provide satisfactory accuracy rates for defuzzified outputs (predictions) when the data has outlier(s). In this study, not only to be able to sort out this problem but also to be able to improve the forecasting accuracy, we propose a combined robust approach for fuzzy time series by assessing how the prediction performance of the methods will be affected from the

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Online fuzzy time series analysis based on entropy discretization and a Fast Fourier Transform

Cited by 89 publications

References 36 publications

A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables

A clustering based forecasting algorithm for multivariable fuzzy time series using linear combinations of independent variables

Rainfall and financial forecasting using fuzzy time series and neural networks based model

A combined robust fuzzy time series method for prediction of time series

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