Robust empirical wavelet fuzzy cognitive map for time series forecasting

Gao, Ruobin; Du, Liang; Yuen, Kum Fai

doi:10.1016/j.engappai.2020.103978

Cited by 43 publications

(21 citation statements)

References 99 publications

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“…Firstly, designing the structure of FCMs by employing common successful strategies including granularity [129], membership values representation [130], fuzzy c-means clustering [37]. Furthermore, wavelet transformation and empirical mode decomposition (EMD) are also proposed to identify FCM's structure and enhance the forecasting performance [36,35,131].…”

Section: General Fcm Model For Time Series Forecastingmentioning

confidence: 99%

“…However [35] developed wavelet-HFCM method to handle large-scale non-stationary times series successfully, it suffers from some weaknesses relating to the wavelet transformation. Regarding this issue, a novel and robust forecasting technique proposed in [131] based on the synergy of HFCM and empirical wavelet transformation (EWT) to boost the performance of conventional FCMs dealing with non-stationary and outliers. EWT used as a novel adaptive signal decomposition method with a significant effect on the model in analyzing non-stationary time series data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Firstly, FCMs still comprise weakness in dealing with the nonstationarity and outliers even though [131] has focused on this issue. Secondly, most of the models have been constructed to deal one step ahead forecasting and very limited number of papers focused on multiple steps ahead forecasting including [132,30].…”

Section: Research Opportunities and Challengesmentioning

confidence: 99%

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Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey

Orang¹,

Silva²,

Guimarães³

2022

Preprint

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Among various soft computing approaches for time series forecasting, Fuzzy Cognitive Maps (FCM) have shown remarkable results as a tool to model and analyze the dynamics of complex systems. FCM have similarities to recurrent neural networks and can be classified as a neuro-fuzzy method. In other words, FCMs are a mixture of fuzzy logic, neural network, and expert system aspects, which act as a powerful tool for simulating and studying the dynamic behavior of complex systems. The most interesting features are knowledge interpretability, dynamic characteristics and learning capability. The goal of this survey paper is mainly to present an overview on the most relevant and recent FCM-based time series forecasting models proposed in the literature. In addition, this article considers an introduction on the fundamentals of FCM model and learning methodologies. Also, this survey provides some ideas for future research to enhance the capabilities of FCM in order to cover some challenges in the real-world experiments such as handling non-stationary data and scalability issues. Moreover, equipping FCMs with fast learning algorithms is one of the major concerns in this area.

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Section: General Fcm Model For Time Series Forecastingmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey

Orang¹,

Silva²,

Guimarães³

2022

Preprint

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“…In general, time series prediction by FCM utilization consists of two stages [18]. Firstly, designing the appropriate structure of FCM using common strategies including granularity [8], membership values representation [19] and Fuzzy c-means clustering [20].…”

Section: Introductionmentioning

confidence: 99%

Introducing Randomized High Order Fuzzy Cognitive Maps as Reservoir Computing Models: A Case Study in Solar Energy and Load Forecasting

Orang¹,

Silva²,

Guimarães³

2022

Preprint

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Fuzzy Cognitive Maps (FCMs) have emerged as an interpretable signed weighted digraph method consisting of nodes (concepts) and weights which represent the dependencies among the concepts. Although FCMs have attained considerable achievements in various time series prediction applications, designing an FCM model with time-efficient training method is still an open challenge. Thus, this paper introduces a novel univariate time series forecasting technique, which is composed of a group of randomized high order FCM models labeled R-HFCM. The novelty of the proposed R-HFCM model is relevant to merging the concepts of FCM and Echo State Network (ESN) as an efficient and particular family of Reservoir Computing (RC) models, where the least squares algorithm is applied to train the model. From another perspective, the structure of R-HFCM consists of the input layer, reservoir layer, and output layer in which only the output layer is trainable while the weights of each sub-reservoir components are selected randomly and keep constant during the training process. As case studies, this model considers solar energy forecasting with public data for Brazilian solar stations as well as Malaysia dataset, which includes hourly electric load and temperature data of the power supply company of the city of Johor in Malaysia. The experiment also includes the effect of the map size, activation function, the presence of bias and the size of the reservoir on the accuracy of R-HFCM method. The obtained results confirm the outperformance of the proposed R-HFCM model in comparison to the other methods. This study provides evidence that FCM can be a new way to implement a reservoir of dynamics in time series modelling.

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“…e authors in [25] used a fuzzy wavelet neural control scheme for the micro-electro-mechanical system (MEMS). A novel time series forecasting model based on fuzzy cognitive maps and empirical wavelet transformation is proposed in [26]. e performance of wavelet neural network (WNN) and ANFIS models was compared using small datasets by [27].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions

Alenezy

Ismail

Wadi

et al. 2021

Journal of Mathematics

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This study aims to model and enhance the forecasting accuracy of Saudi Arabia stock exchange (Tadawul) data patterns using the daily stock price indices data with 2026 observations from October 2011 to December 2019. This study employs a nonlinear spectral model of maximum overlapping discrete wavelet transform (MODWT) with five mathematical functions, namely, Haar, Daubechies (Db), Least Square (LA-8), Best localization (BL14), and Coiflet (C6) in conjunction with adaptive network-based fuzzy inference system (ANFIS). We have selected oil price (Loil) and repo rate (Repo) as input values according to correlation, the Engle and Granger Causality test, and multiple regressions. The input variables in this study have been collected from Saudi Authority for Statistics and Saudi Central Bank. The output variable is obtained from Tadawul. The performance of the proposed model (MODWT-LA8-ANFIS) is evaluated in terms of mean error (ME), root mean square error (RMSE), and mean absolute percentage error (MAPE). Also, we have compared the MODWT-LA8-ANFIS model with traditional models, which are autoregressive integrated moving average (ARIMA) model and ANFIS model. The obtained results show that the performance of MODWT-LA8-ANFIS is better than that of the traditional models. Therefore, the proposed forecasting model is capable of decomposing in the stock markets.

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Robust empirical wavelet fuzzy cognitive map for time series forecasting

Cited by 43 publications

References 99 publications

Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey

Time Series Forecasting Using Fuzzy Cognitive Maps: A Survey

Introducing Randomized High Order Fuzzy Cognitive Maps as Reservoir Computing Models: A Case Study in Solar Energy and Load Forecasting

Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions

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