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https://www.sciencedirect.com/science/article/pii/S0169743903001114
Nov 28, 2003 · The goal of this paper is to use a support vector machine (SVM) for the task of time series prediction. SVM is a relatively new nonlinear technique in the field of chemometrics and it has been shown to perform well for classification tasks [2] , regression [3] and time series prediction [4] .Cited by: 427
https://www.researchgate.net/publication/223045088_Using_support_vector_machines_for_time_series_prediction
Using support vector machines for time series prediction Article in Chemometrics and Intelligent Laboratory Systems 69(1-2):35-49 · November 2003 with 343 Reads How we measure 'reads'
https://www.sciencedirect.com/science/article/abs/pii/S0169743903001114
Nov 28, 2003 · This creates the possibility to give early warnings of possible process malfunctioning. In this paper, time series prediction is performed by support vector machines (SVMs), Elman recurrent neural networks, and autoregressive moving average (ARMA) models. A comparison of these three methods is made based on their predicting ability.Cited by: 427
https://www.researchgate.net/publication/224408260_Time_Series_Prediction_Using_Support_Vector_Machines_A_Survey
On the side of financial time series analysis, Sapankevych and Sankar [16] made a survey on the SVM (Support Vector Machines) and focused on times series prediction using SVM. Kim [11] is an ...
https://pdfs.semanticscholar.org/52ad/70ac59c20a1d091db409c94d421d4d7b273c.pdf
A Survey of Time Series Prediction Using SVM Yongning Ma October, 1 2012 Abstract This expository paper is a result of the reading project for our knowl-edge engineering class.1 Our task is to read a survey on time series pre-diction [20] and identify the key ideas of the paper. Based on our under-
https://www.infona.pl/resource/bwmeta1.element.ieee-art-000004840324
Time series prediction techniques have been used in many real-world applications such as financial market prediction, electric utility load forecasting , weather and environmental state prediction, and reliability forecasting. The underlying system models and time series data generating processes are generally complex for these applications and the models for these systems are usually not ...Cited by: 698
https://ieeexplore.ieee.org/document/4840324
Apr 24, 2009 · Time Series Prediction Using Support Vector Machines: A Survey ... such as multi-layer perceptrons.The ultimate goal is to provide the reader with insight into the applications using SVM for time series prediction, to give a brief tutorial on SVMs for time series prediction, to outline some of the advantages and challenges in using SVMs for ...Cited by: 698
http://philjournalsci.dost.gov.ph/images/pdf/pjs_pdf/vol146no2/time_series_link_prediction_using_support_vector_machines.pdf
Key words: classification, link prediction, new links, support vector machine, vector auto regression Time-Series Link Prediction Using Support Vector Machines Jan Miles Co* and Proceso Fernandez Department of Information Systems and Computer Science Ateneo …
https://link.springer.com/chapter/10.1007%2FBFb0020283
Jun 09, 2005 · Abstract. Support Vector Machines are used for time series prediction and compared to radial basis function networks. We make use of two different cost functions for Support Vectors: training with (i) an e insensitive loss and (ii) Huber's robust loss function and discuss how to choose the regularization parameters in these models.Cited by: 1120
https://dl.acm.org/citation.cfm?id=1721761
This paper provides a survey of time series prediction applications using a novel machine learning approach: Support Vector Machines (SVM). The underlying motivation for using SVMs is the ability of this methodology to accurately forecast time series data when the underlying system processes are typically nonlinear, non-stationary and not ...Cited by: 698
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