Time Series Prediction Using Support Vector Machines: A Survey

Sapankevych, Nicholas I.; Sankar, Ravi

doi:10.1109/mci.2009.932254

Cited by 885 publications

(369 citation statements)

References 65 publications

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“…Recent literature suggests Support Vector Regression (SVR) as one of the most effective models to forecast future energy consumption [9], [32]. Other well established methods are Linear Regression and MultiLayer Perceptron (MLP).…”

Section: B Learning Algorithmsmentioning

confidence: 99%

“…Many techniques for energy consumption prediction have been inspired by research on statistical and machine learning, from Linear Regression [16], [30], ARMA [18], [34], and Generalized Additive Models [4], [10], [41] to Neural Networks [3], [15], [23] and Support Vector Regression [9], [32]. However, these techniques have been typically used at very large space scales, such as predicting the electrical load of a market segment serving thousands of customers or even an entire country.…”

Section: Introductionmentioning

confidence: 99%

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Cluster-based aggregate forecasting for residential electricity demand using smart meter data

Wijaya

Vasirani

Humeau

et al. 2015

2015 IEEE International Conference on Big Data (Big Data)

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Abstract-While electricity demand forecasting literature has focused on large, industrial, and national demand, this paper focuses on short-term (1 and 24 hour ahead) electricity demand forecasting for residential customers at the individual and aggregate level. Since electricity consumption behavior may vary between households, we first build a feature universe, and then apply Correlation-based Feature Selection to select features relevant to each household. Additionally, smart meter data can be used to obtain aggregate forecasts with higher accuracy using the so-called Cluster-based Aggregate Forecasting (CBAF) strategy, i.e., by first clustering the households, forecasting the clusters' energy consumption separately, and finally aggregating the forecasts. We found that the improvement provided by CBAF depends not only on the number of clusters, but also more importantly on the size of the customer base.

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Section: B Learning Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cluster-based aggregate forecasting for residential electricity demand using smart meter data

Wijaya

Vasirani

Humeau

et al. 2015

2015 IEEE International Conference on Big Data (Big Data)

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show abstract

“…The generalized comparison made by Abraham and Yeung [4], Härdle [5], Sapankevych and Sangar [6], Khandani [7], Öğüt [8] and Papadimitriou [9] confirmed that the SVR is a state-of-art technique in exchange rate forecasting. Lam [10] pointed out that machine learning methods regarded numeric data directly as input, which made it contain more information than classical methodologies.…”

Section: Introductionmentioning

confidence: 78%

Hybrid Genetic Algorithm and Support Vector Regression Performance in CNY Exchange Rate Prediction

Feng¹,

Wu²

2016

Proceedings of the 2016 International Conference on Engineering Science and Management

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“…Sapankevych and Sankar [9] have done a comprehensive survey of time-series prediction techniques using support vector machines. Most of the prediction techniques are based on machine learning that learn a nonlinear model from the data.…”

Section: Literature Survey Introductionmentioning

confidence: 99%

Learning and prediction of relational time series

Tan

Darken

2015

Comput Math Organ Theory

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Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to 12a. DISTRIBUTION / AVAILABILITY STATEMENTApproved for public release; distribution is unlimited 12b. DISTRIBUTION CODE ABSTRACT (maximum 200 words)Prediction of events is fundamental to both human and artificial agents. The main problem with previous prediction techniques is that they cannot predict events that have never been experienced before. This dissertation addresses the problem of predicting such novelty by developing algorithms and computational models inspired from recent cognitive science theories: conceptual blending theory and event segmentation theory. We were able to show that prediction accuracy for event or state prediction can be significantly improved using these methods.The main contribution of this dissertation is a new class of prediction techniques inspired by conceptual blending that improves prediction accuracy overall and has the ability to predict even events that have never been experienced before. We also show that event segmentation theory, when integrated with these techniques, results in greater computational efficiency. We implemented the new prediction techniques, and more traditional alternatives such as Markov and Bayesian techniques, and compared their prediction accuracy quantitatively for three domains: a roleplaying game, intrusion-system alerts, and event prediction of maritime paths in a discrete-event simulator. Other contributions include two new unification algorithms that improve over a naïve one, and an exploration of ways to maintain a minimum-size knowledge base without affecting prediction accuracy. SUBJECT TERMS ABSTRACTPrediction of events is fundamental to both human and artificial agents. The main problem with previous prediction techniques is that they cannot predict events that have never been experienced before. This dissertation addresses the problem of predicting such novelty by developing algorithms and computational models inspired from recent cognitive science theories: conceptual blending theory and event segmentation theory.We were able to show that prediction accuracy for event or state prediction can be significantly improved using these methods.The main contribution of this dissertation is a new class of prediction techniques inspired by conceptual blending that improves prediction accuracy overall and has the ability to predict even events that have never been experienced before. We also show that event segmentation theory, when integrated with these techniques, results in greater computational efficiency. We implemented the new prediction techniques, and more traditional alternatives such as Markov and Bayesian t...

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Time Series Prediction Using Support Vector Machines: A Survey

Cited by 885 publications

References 65 publications

Cluster-based aggregate forecasting for residential electricity demand using smart meter data

Cluster-based aggregate forecasting for residential electricity demand using smart meter data

Hybrid Genetic Algorithm and Support Vector Regression Performance in CNY Exchange Rate Prediction

Learning and prediction of relational time series

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