Time series forecasting based on kernel mapping and high-order fuzzy cognitive maps

Yuan, Kaixin; Jing, Liu; Yang, Shiwu; Wu, Kai; Shen, Fang

doi:10.1016/j.knosys.2020.106359

Cited by 38 publications

(8 citation statements)

References 31 publications

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“…Finally, based on the idea of fuzzy time series forecasting, a long-term forecast of the time series is made. Yuan, K. X [53] proposed a time series prediction model Kernel-HFCM based on kernel mapping and highorder fuzzy cognitive map (HFCM). He mapped the original one-dimensional time series to a multi-dimensional feature time series, and then extracted the key features of time series.…”

Section: )Fuzzy Time Series Forecasting Methodsmentioning

confidence: 99%

Forecast Methods for Time Series Data: A Survey

et al. 2021

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Research on forecasting methods of time series data has become one of the hot spots. More and more time series data are produced in various fields. It provides data for the research of time series analysis method, and promotes the development of time series research. Due to the generation of highly complex and large-scale time series data, the construction of forecasting models for time series data brings greater challenges. The main challenges of time series modeling are high complexity of time series data, low accuracy and poor generalization ability of prediction model. This paper attempts to cover the existing modeling methods for time series data and classify them. In addition, we make comparisons between different methods and list some potential directions for time series forecasting.

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Section: )Fuzzy Time Series Forecasting Methodsmentioning

confidence: 99%

Forecast Methods for Time Series Data: A Survey

et al. 2021

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“…As instances [35] applied Harr wavelet to extract the features time series or fuzzy c-means in [155]. To deal with some limitations of proposed feature time series models, a novel and generalized feature time series extraction method is suggested bt [156] merging kernel mapping and HFCM that has been inspired by the kernel methods and the support vector regression (SVR), referred to as kernel HFCM. Kernel mapping is defined to transfer the original one-dimensional time series into the multidimensional feature time series, and then key feature time series (KFTS) from the multidimensional feature time series are selected through the proposed feature selection algorithm to develop HFCM.…”

Section: Literature Reviewmentioning

confidence: 99%

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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“…Noteworthy that FCMs as qualitative soft computing techniques can be used to represent the dynamic behaviour of complex systems with high ability to dealing with uncertainties [7]. Therefore a wide range of FCM-based time series forecasting methods have been developed in the literature [8,9,10,11,12,13,14,15,16,17].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, evolutionary learning has been replaced with other techniques in some references. For instance, ridge regression in [13,17,40], Bayesian ridge regression in [21], Moore-Penrose inverse in [16]. A rapid and robust learning method with maximum entropy was proposed in [41] to learn large scale FCMs composed of least-squares and maximum entropy terms.…”

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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Time series forecasting based on kernel mapping and high-order fuzzy cognitive maps

Cited by 38 publications

References 31 publications

Forecast Methods for Time Series Data: A Survey

Forecast Methods for Time Series Data: 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

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