Parsimonious fuzzy time series modelling

Gao, Ruobin; Duru, Okan

doi:10.1016/j.eswa.2020.113447

Cited by 36 publications

(14 citation statements)

References 36 publications

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“…Furthermore, the application of the fuzzy time-series approach [ 31 ] to our proposed method can be considered as efficient because the extracted envelopes include the uncertainty and/or imprecision, which can be efficiently dealt with using fuzzy modeling. In particular, recently proposed fuzzy time-series models [ 32 , 33 ] could possibly improve the robustness and accuracy of the proposed method; therefore, investigating their applicability is an important direction of future study.…”

Section: Discussionmentioning

confidence: 99%

Utilization of Micro-Doppler Radar to Classify Gait Patterns of Young and Elderly Adults: An Approach Using a Long Short-Term Memory Network

Hayashi

Saho

Shioiri

et al. 2021

Sensors

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To develop a daily monitoring system for early detection of fall risk of elderly people during walking, this study presents a highly accurate micro-Doppler radar (MDR)-based gait classification method for the young and elderly adults. Our method utilizes a time-series of velocity corresponding to leg motion during walking extracted from the MDR spectrogram (time-velocity distribution) in an experimental study involving 300 participants. The extracted time-series was inputted to a long short-term memory recurrent neural network to classify the gaits of young and elderly participant groups. We achieved a classification accuracy of 94.9%, which is significantly higher than that of a previously presented velocity-parameter-based classification method.

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Section: Discussionmentioning

confidence: 99%

Utilization of Micro-Doppler Radar to Classify Gait Patterns of Young and Elderly Adults: An Approach Using a Long Short-Term Memory Network

Hayashi

Saho

Shioiri

et al. 2021

Sensors

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“…e time series prediction method and time trend extrapolation method belong to linear forecast methods [25,26]. However, linear forecast methods may produce a large deviation when there are great changes in the external environment such as weather or temperature [27,28]. Nonlinear forecast methods are more suitable for PV power output prediction, which generally employ artificial neural network (ANN), Gaussian process regression (GPR), extreme learning machine (ELM), support vector machine (SVM), and more [29,30].…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short‐Term Power Prediction in Photovoltaic Power Generation System

Kan

Lin

et al. 2021

Computational Intelligence and Neuroscience

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When a photovoltaic (PV) system is connected to the electric power grid, the power system reliability may be exposed to a threat due to its inherent randomness and volatility. Consequently, predicting PV power generation becomes necessary for reasonable power distribution scheduling. A hybrid model based on an improved bird swarm algorithm (IBSA) with extreme learning machine (ELM) algorithm, i.e., IBSAELM, was developed in this study for better prediction of the short-term PV output power. The IBSA model was initially used to optimize the hidden layer threshold and input weight of the ELM model. Further, the obtained optimal parameters were input into the ELM model for predicting short-term PV power. The results revealed that the IBSAELM model is superior in terms of the prediction accuracy compared to existing methods, such as support vector machine (SVM), back propagation neural network (BP), Gaussian process regression (GPR), and bird swarm algorithm with extreme learning machine (BSAELM) models. Accordingly, it achieved great benefits in terms of the utilization efficiency of whole power generation. Furthermore, the stability of the power grid was well maintained, resulting in balanced power generation, transmission, and electricity consumption.

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“…However, all these models still have many disadvantages in forecasting in real situations. The traditional methods could not handle the forecasting problems in which the historical data presented as approximate numerical forecast values [28]. A comparison of the SVRbased and ARIMA-based methods, each having their own merits and weaknesses, has not been undertaken in the field of forecasting the approximate numerical forecast values of historical data.…”

Section: Introductionmentioning

confidence: 99%

Application of Fuzzy-Based Support Vector Regression to Forecast of International Airport Freight Volumes

et al. 2022

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As freight volumes increase, airports are likely to require additional infrastructure development, increased air services, and expanded facilities. Prediction of freight volumes could ensure effective investment. Among the computational intelligence models, support vector regression (SVR) has become the dominant modeling paradigm. In this study, a fuzzy-based SVR (FSVR) model was used to solve the freight volume prediction problem in international airports. The FSVR model can use a fuzzy time series of historical traffic changes for predictions. A fuzzy classification algorithm was used for elements of similar levels in the time series to appropriately divide traffic changes into fuzzy sets, generate membership function values, and establish a fuzzy relationship to produce a fuzzy interpolation with a minimal error. A comparison of the FSVR model with other models revealed that the FSVR model had the lowest mean absolute percentage error (all <2.5%), mean absolute error, and root mean square error for all types of traffic at all the analyzed airports. Fuzzy sets can handle uncertainty and imprecision in time series. Therefore, the prediction accuracy of the entire time series model is improved by taking advantage of SVR and fuzzy sets. By using the highly accurate FSVR model to predict the future growth of air freight volume, airport management could analyze their existing facilities and service capacity to identify operational bottlenecks and plan future development. The FSVR model is the most accurate forecasting model for air traffic forecasting.

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Parsimonious fuzzy time series modelling

Cited by 36 publications

References 36 publications

Utilization of Micro-Doppler Radar to Classify Gait Patterns of Young and Elderly Adults: An Approach Using a Long Short-Term Memory Network

Utilization of Micro-Doppler Radar to Classify Gait Patterns of Young and Elderly Adults: An Approach Using a Long Short-Term Memory Network

Hybrid Improved Bird Swarm Algorithm with Extreme Learning Machine for Short‐Term Power Prediction in Photovoltaic Power Generation System

Application of Fuzzy-Based Support Vector Regression to Forecast of International Airport Freight Volumes

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