SVM hyperparameters tuning for recursive multi-step-ahead prediction

Liu, Jie; Zio, Enrico

doi:10.1007/s00521-016-2272-1

Cited by 30 publications

(18 citation statements)

References 46 publications

(58 reference statements)

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“…However, this method is difficult to consider various influential factors comprehensively, so it is difficult to select independent variables, and the relationship between variables always changes with the change of internal and external environment, so it lacks self-learning ability. Literature [25], [32] uses regression analysis method to forecast natural gas load scientifically.…”

Section: ② Regression Analysis Methodsmentioning

confidence: 99%

“…They come from China University of Petroleum, Milan Polytechnic University, Paris Sacre University and other institutions. The research mainly focuses natural gas load forecasting, multi-objective optimization and decisionmaking [23][24] [25]. The team uses wavelet transform, deep neural network, data-driven demand side management method and other methods and technologies to realize natural gas load forecasting.…”

Section: Distribution Of Academic Communitymentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Research Progress and Hot Front of Natural Gas Load Forecasting From Technical Perspective

et al. 2020

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Section: ② Regression Analysis Methodsmentioning

confidence: 99%

Section: Distribution Of Academic Communitymentioning

confidence: 99%

A Survey of Research Progress and Hot Front of Natural Gas Load Forecasting From Technical Perspective

et al. 2020

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“…A recurrent challenge found in the literature is the correct tuning of the

parameter when a Gaussian kernel is used in the SVM [ 39 , 40 , 41 ]. This problem has been addressed with different strategies, mainly metaheuristic optimization techniques that consume significant time and computational resources, which is why it remains an open issue in the machine learning area.…”

Section: Methodsmentioning

confidence: 99%

A Novel Method for Objective Selection of Information Sources Using Multi-Kernel SVM and Local Scaling

Areiza-Laverde

Castro-Ospina

Hernández

et al. 2020

Sensors

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Advancement on computer and sensing technologies has generated exponential growth in the data available for the development of systems that support decision-making in fields such as health, entertainment, manufacturing, among others. This fact has made that the fusion of data from multiple and heterogeneous sources became one of the most promising research fields in machine learning. However, in real-world applications, to reduce the number of sources while maintaining optimal system performance is an important task due to the availability of data and implementation costs related to processing, implementation, and development times. In this work, a novel method for the objective selection of relevant information sources in a multimodality system is proposed. This approach takes advantage of the ability of multiple kernel learning (MKL) and the support vector machines (SVM) classifier to perform an optimal fusion of data by assigning weights according to their discriminative value in the classification task; when a kernel is designed for representing each data source, these weights can be used as a measure of their relevance. Moreover, three algorithms for tuning the Gaussian kernel bandwidth in the classifier prediction stage are introduced to reduce the computational cost of searching for an optimal solution; these algorithms are an adaptation of a common technique in unsupervised learning named local scaling. Two real application tasks were used to evaluate the proposed method: the selection of electrodes for a classification task in Brain–Computer Interface (BCI) systems and the selection of relevant Magnetic Resonance Imaging (MRI) sequences for detection of breast cancer. The obtained results show that the proposed method allows the selection of a small number of information sources.

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“…Liu and Enrico 37 analyses the possible objectives of the optimization for tuning hyperparameters. Related to the prediction of wind speed, in a region and leakage from the reactor coolant pump in a nuclear power plant, experiments on one synthetic time series data and two real‐time series data show that the bi‐objective optimization combining the MAD and the prediction accuracy (MSE or MAPE) on all the steps is a preferable choice for SVM hyperparameters tuning for recursive multi‐step‐ahead prediction.…”

Section: Lf‐dcwqpso Algorithmmentioning

confidence: 99%

An improved quantum particle swarm photovoltaic multi‐peak mPPT method combined with Lévy flight

Chen

Zhang

et al. 2020

Energy Science & Engineering

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SVM hyperparameters tuning for recursive multi-step-ahead prediction

Abstract: International audienc

Cited by 30 publications

References 46 publications

A Survey of Research Progress and Hot Front of Natural Gas Load Forecasting From Technical Perspective

A Survey of Research Progress and Hot Front of Natural Gas Load Forecasting From Technical Perspective

A Novel Method for Objective Selection of Information Sources Using Multi-Kernel SVM and Local Scaling

An improved quantum particle swarm photovoltaic multi‐peak mPPT method combined with Lévy flight

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