Detection of Structural Breaks in Time Series Using Fuzzy Techniques

Novák, Vilém

doi:10.5391/ijfis.2018.18.1.1

Cited by 16 publications

(4 citation statements)

References 24 publications

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“…FuzzyEn is a metric of the complexity of time series [34][35][36]. It measures the complexity of the series by the probability that the time series produces a new pattern as the embedding dimension changes.…”

Section: Fuzzy Entropymentioning

confidence: 99%

Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy

Qin

Zhou

2019

Applied Sciences

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Accurate wind speed forecasting is of great significance for a reliable and secure power generation system. In order to improve forecasting accuracy, this paper introduces the LSTM neural network and proposes a wind speed statistical forecasting method based on the EEMD-FuzzyEn-LSTMNN model. Moreover, the MIC is used to analyze the autocorrelation of wind speed series, and the predictable time of wind speed statistical forecasting method for direct multistep forecasting is taken as four hours. In the EEMD-FuzzyEn-LSTMNN model, the original wind speed series is firstly decomposed into a series of components by using EEMD. Then, the FuzzyEn is used to calculate the complexity of each component, and the components with similar FuzzyEn values are classified into one group. Finally, the LSTMNN model is used to forecast each subsequence after classification. The forecasting result of the original wind speed series is obtained by aggregating the forecasting result of each subsequence. Three forecasting cases under different terrain conditions were selected to validate the proposed model, and the BPNN model, the SVM model and the LSTMNN model were used for comparison. The experimental results show that the forecasting accuracy of the EEMD-FuzzyEn-LSTMNN model is much higher than that of the other three models.

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Section: Fuzzy Entropymentioning

confidence: 99%

Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy

Qin

Zhou

2019

Applied Sciences

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“…While the former uses another artificial neural network to forecast production based on energy inputs, another decision making analysis has been done in [66]. Several prediction procedures on case basis has been done by authors in [67][68][69][70]. The above stated method provided prediction using support vector machines based on soil properties.…”

Section: The Need Of This Frameworkmentioning

confidence: 99%

Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction

Jain

Kapania

et al. 2018

Symmetry

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Recently, prediction modelling has become important in data analysis. In this paper, we propose a novel algorithm to analyze the past dataset of crop yields and predict future yields using regression-based approximation of time series fuzzy data. A framework-based algorithm, which we named DAbFP (data algorithm for degree approximation-based fuzzy partitioning), is proposed to forecast wheat yield production with fuzzy time series data. Specifically, time series data were fuzzified by the simple maximum-based generalized mean function. Different cases for prediction values were evaluated based on two-set interval-based partitioning to get accurate results. The novelty of the method lies in its ability to approximate a fuzzy relation for forecasting that provides lesser complexity and higher accuracy in linear, cubic, and quadratic order than the existing methods. A lesser complexity as compared to dynamic data approximation makes it easier to find the suitable de-fuzzification process and obtain accurate predicted values. The proposed algorithm is compared with the latest existing frameworks in terms of mean square error (MSE) and average forecasting error rate (AFER).

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“…The soft computing techniques employed in this framework are mostly combinations of artificial neural networks, evolutionary algorithms, fuzzy and rough sets. These approaches are widely used for crisp or fuzzy forecasts based on crisp past observations such as electricity load, stock index prices and temperature (for a review of these techniques, we refer to [48][49][50][51][52][53][54][55][56]). In addition, various methods combine techniques of time series and fuzzy regression analysis [57].…”

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confidence: 99%

A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data

2023

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In this paper, a nonlinear time series model is developed for the case when the underlying time series data are reported by LR fuzzy numbers. To this end, we present a three-stage nonparametric kernel-based estimation procedure for the center as well as the left and right spreads of the unknown nonlinear fuzzy smooth function. In each stage, the nonparametric Nadaraya–Watson estimator is used to evaluate the center and the spreads of the fuzzy smooth function. A hybrid algorithm is proposed to estimate the unknown optimal bandwidths and autoregressive order simultaneously. Various goodness-of-fit measures are utilized for performance assessment of the fuzzy nonlinear kernel-based time series model and for comparative analysis. The practical applicability and superiority of the novel approach in comparison with further fuzzy time series models are demonstrated via a simulation study and some real-life applications.

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Detection of Structural Breaks in Time Series Using Fuzzy Techniques

Cited by 16 publications

References 24 publications

Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy

Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy

Degree Approximation-Based Fuzzy Partitioning Algorithm and Applications in Wheat Production Prediction

A Three-Stage Nonparametric Kernel-Based Time Series Model Based on Fuzzy Data

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