research-article

Time series prediction using support vector machines: a survey

Authors:

Nicholas I. Sapankevych,

Ravi SankarAuthors Info & Claims

IEEE Computational Intelligence Magazine, Volume 4, Issue 2

Pages 24 - 38

https://doi.org/10.1109/MCI.2009.932254

Published: 01 May 2009 Publication History

Abstract

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 known a priori. Accurate and unbiased estimation of the time series data produced by these systems cannot always be achieved using well known linear techniques, and thus the estimation process requires more advanced time series prediction algorithms. 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 defined a-priori. SVMs have also been proven to outperform other non-linear techniques including neural-network based non-linear prediction techniques 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 time series prediction, and to provide a source for the reader to locate books, technical journals, and other online SVM research resources.

References

[1]

S. J. Orfanidis, Optimum Signal Processing: An Introduction, 2nd ed. New York: Mac-Millan, 1988.

[2]

R. E. Kalman, "A new approach to linear filtering and prediction problems," Trans. ASME, J. Basic Eng., ser. D, vol. 82, pp. 35-45, 1960.

[3]

V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.

[4]

V. N. Vapnik, Statistical Learning Theory. New York: Wiley, 1998.

[5]

N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge, U.K.: Cambridge Univ. Press, 2000.

Digital Library

[6]

V. N. Vapnik, "An overview of statistical learning theory," IEEE Trans. Neural Netw., vol. 10, no. 5, pp. 988-999, Sept. 1999.

Digital Library

[7]

H. Drucker, C. C. J. Burges, L. Kaufman, A. Smola, and V. Vapnik, "Support vector regression machines," Adv. Neural Inform. Process. Syst., no. 9, pp. 155-161, 1997.

[8]

B. Schölkopf, A. J. Smola, and C. Burges, Advances in Kernel Methods--Support Vector Learning. Cambridge, MA: MIT Press, 1999.

Digital Library

[9]

J. A. K. Suykens and J. Vandewalle, "Least squares support vector machine classifiers," Neural Process. Lett., vol. 9, no. 3, pp. 293-300, June 1999.

Digital Library

[10]

J. A. K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, and J. Vandewalle, Least Squares Support Vector Machines. Singapore: World Scientific, 2002.

[11]

K.-R. Müller, A. J. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, and V. Vapnik, "Predicting time series with support vector machines," in Proc. Int. Conf. on Artificial Neural Networks, Springer, 1997.

[12]

S. Mukherjee, E. Osuna, and F. Girosi, "Nonlinear prediction of chaotic time series using support vector machines," in Proc. 1997 IEEE Workshop - Neural Networks for Signal Processing VII, Sept. 24-26, 1997, pp. 511-520.

[13]

A. J. Smola and B. Schölkopf, "A tutorial on support vector regression," Royal Holloway College, London, UK, NeuroCOLT Tech. Rep., 1998.

[14]

N. de Freitas, M. Milo, P. Clarkson, M. Niranjan, and A. Gee, "Sequential support vector machines," in Proc. 1999 IEEE Signal Processing Society Workshop - Neural Networks for Signal Processing IX, Aug. 23-25, 1999, pp. 31-40.

[15]

S. Rüping, "SVM kernels for time series analysis," CS Dept., AI Unit, University of Dortmund, Dortmund, Germany, 2001.

[16]

K. L. Lee and S. A. Billings, "Time series prediction using support vector machines, the orthogonal and regularized orthogonal least-squares algorithms," Int. J. Syst. Sci., vol. 33, no. 10, pp. 811-821, 2002.

[17]

L. Cao and Q. Gu, "Dynamic support vector machines for non-stationary time series forecasting," Intell. Data Anal., vol. 6, no. 1, pp. 67-83, 2002.

Digital Library

[18]

J.-Y. Zhu, B. Ren, H.-X. Zhang, and Z.-T. Deng, "Time series prediction via new support vector machines," in Proc. 1st Int. Conf. on Machine Learning and Cybernetics, Nov. 2002, vol. 1, pp. 364-366.

[19]

S. Rüping and K. Morik, "Support vector machines and learning about time," 2003 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2003), Apr. 6-10, 2003, vol. 4, pp. IV-864-IV-867.

[20]

L. J. Cao, "Support vector machine experts for time series prediction," Neurocomputing , vol. 51, pp. 321-339, Apr. 2003.

[21]

Y. Mei-Ying and W. Xiao-Dong, "Chaotic time series prediction using least squares support vector machines," Chin. Phys., vol. 13, no. 4, pp. 454-458, Apr. 2004.

[22]

J. M. Górriz, C. G. Puntonet, M. Salmerón, R. Martin-Clemente, and S. Hornillo-Mellado, "Using confidence interval of a regularization network," in Proc. 12th IEEE Mediterranean Electrotechnical Conf. (MELECON 2004), May 12-15, 2004, pp. 343-346.

[23]

A. Hornstein and U. Parlitz, "Bias reduction for time series models based on support vector regression," Int. J. Bifurcation Chaos, vol. 14, no. 6, pp. 1947-1956, 2004.

[24]

J. M. Górriz, C. G. Puntonet, M. Salmerón, and J. J. G. de la Rosa, "A new model for time series forecasting using radial basis functions and exogenous data," Neural Comput. Appl., vol. 13, no. 2, pp. 101-111, June 2004.

Digital Library

[25]

A. J. Smola and B. Schölkopf, "A tutorial on support vector regression," Stat. Comput. , vol. 14, no. 3, pp. 199-222, Aug. 2004.

Digital Library

[26]

Y.-F. Deng, X. Jin, and Y.-X. Zhong, "Ensemble SVR for prediction of time series," in Proc. 4th Int. Conf. on Machine Learning and Cybernetics, Aug. 18-21, 2005, pp. 3528-3534.

[27]

J. An, Z.-O. Wang, Q. Yang, and Z. Ma, "A SVM function approximation approach with good performances in interpolation and extrapolation," in Proc. 4th Int. Conf. on Machine Learning and Cybernetics, Aug. 18-21, 2005, pp. 1648-1653.

[28]

T. B. Trafalis and H. Ince, "Support vector machine for regression and applications to financial forecasting," in Proc. IEEE-INNS-ENNS Int. Joint Conf. on Neural Networks 2000 (IJCNN 2000), vol. 6, July 24-27, 2000, pp. 348-353.

[29]

F. E. H. Tay and L. J. Cao, "Application of support vector machines in financial time series forecasting," Omega, vol. 29, pp. 309-317, 2001.

[30]

T. Van Gestel, J. A. K. Suykens, D.-E. Baestaens, A. Lambrechts, G. Lanckriet, B. Vandaele, B. De Moor, and J. Vandewall, "Financial time series prediction using least squares support vector machines within the evidence framework," IEEE Trans. Neural Netw., vol. 12, no. 4, pp. 809-821, July 2001.

Digital Library

[31]

F. E. H. Tay and L. J. Cao, "Improved financial time series forecasting by combining support vector machines with self-organizing feature map," Intell. Data Anal., vol. 5, no. 4, pp. 339-354, 2001.

Digital Library

[32]

F. E. H. Tay and L. J. Cao, "Modified support vector machines in financial time series forecasting," Neurocomputing, vol. 48, pp. 847-861, Oct. 2002.

[33]

F. E. H. Tay and L. J. Cao, "e-descending support vector machines for financial time series forecasting," Neural Process. Lett., vol. 15, no. 2, pp. 179-195, 2002.

Digital Library

[34]

H. Yang, I. King, and L. Chan, "Non-fixed and asymetrical margin approach to stock market prediction using support vector regression," in Proc. 9th Int. Conf. on Neural Information Processing (ICONIP '02), Nov. 18-22, 2002, vol. 3, pp. 1398-1402.

[35]

H. Yang, L. Chan, and I. King, "Support vector machine regression for volatile stock market prediction," in Proc. 3rd Int. Conf. on Intelligent Data Engineering and Automated Learning (IDEAL'02), Springer-Verlag, 2002, pp. 391-396.

[36]

A. Abraham, N. S. Philip, and P. Saratchandran, "Modeling chaotic behavior of stock indices using intelligent paradigms," Int. J. Neural, Parallel, Scientific Computat., vol. 11, no. 1/2, pp. 143-160, 2003.

Digital Library

[37]

H. Yang, "Margin variations in support vector regression for the stock market prediction", Ph.D. dissertation, Chinese Univ. of Hong Kong, June 2003.

[38]

P. Ongsritrakul and N. Soonthornphisaj, "Apply decision tree and support vector regression to predict the gold price," in Proc. Int. Joint Conf. on Neural Networks - 2003, July 20-24, 2003, vol. 4, pp. 2488-2492.

[39]

L. J. Cao and F. E. H. Tay, "Support vector machine with adaptive parameters in financial time series forecasting," IEEE Trans. Neural Netw., vol. 14, no. 6, pp. 1506-1518, Nov. 2003.

Digital Library

[40]

Y. Liang and Y. Sun, "An improved method of support vector machine and its application to financial time series forecasting," Prog. Natural Sci., vol. 13, no. 9, pp. 696-700, 2003.

[41]

A. Abraham and A. A. Yeung, "Integrating ensemble of intelligent systems for modeling stock indices," Lect. Notes Comput. Sci., vol. 2687, pp. 774-781, 2003.

Digital Library

[42]

K.-J. Kim, "Financial time series forecasting using support vector machines," Neurocomputing , vol. 55, pp. 307-319, 2003.

[43]

Y. Bao, Y. Lu, and J. Zhang, "Forecasting stock price by SVMS regression," Lect. Notes Comput. Sci., vol. 3192, pp. 295-303, 2004.

[44]

H. Yang, K. Huang, L. Chan, I. King, and M. R. Lyu, "Outliers treatment in support vector regression for financial time series prediction," in Proc. 11th Int. Conf. on Neural Information Processing (ICONIP 2004), Lecture Notes in Computer Science, 2004, vol. 3316, pp. 1260-1265.

[45]

W. Huang, Y. Nakamori, and S.-Y. Wang, "Forecasting stock market movement direction with support vector machine," Comput. Operat. Res., vol. 32, no. 10, pp. 2513- 2522, Oct. 2005.

Digital Library

[46]

Y.-K. Bao, Z.-T. Liu, L. Guo, and W. Wang, "Forecasting stock composite index by fuzzy support vector machines regression," in Proc. 4th Int. Conf. on Machine Learning and Cybernetics, Aug. 18-21, 2005, pp. 3535-3540.

[47]

D.-Z. Cao, S.-L. Pang, and Y.-H. Bai, "Forecasting exchange rate using support vector machines," in Proc. 4th Int. Conf. on Machine Learning and Cybernetics, Aug. 18-21, 2005, pp. 3448-3452.

[48]

T. Z. Tan, C. Quek, and G. S. Ng, "Brain-inspired genetic complimentary learning for stock market prediction," in Proc. 2005 IEEE Congress on Evolutionary Computation, Sept. 2-5, 2005, vol. 3, pp. 2653-2660.

[49]

D. C. Sansom, T. Downs, and T. K. Saha, "Evaluation of support vector machine based forecasting tool in electricity price forecasting for australian national electricity market participants," presented at the Australian Universities Power Engineering Conf., 2002.

[50]

Z. Huang, H. Chen, C.-J. Hsu, W.-H. Chen, and S. Wu, "Credit rating analysis with support vector machines and neural networks: A market comparative study," Decision Support Syst., vol. 37, pp. 543-558, 2004.

Digital Library

[51]

Y. Hur and S. Lim, "Customer churning prediction using support vector machines in online auto insurance service," in Proc. 2nd Int. Symp. on Neural Networks (ISNN 2005), Lecture Notes in Computer Science, May 30-June 1, 2005, vol. 3497, pp. 928-933.

[52]

I. Bose and R. Pal, "Using support vector machines to evaluate financial fate of dotcoms," in Proc. Pacific Asia Conf. on Information Systems 2005, July 7-10, 2005, pp. 521-528.

[53]

P.-F. Pai and C.-S. Lin, "Using support vector machines to forecast the production values of the machinery industry in Taiwan," Int. J. Adv. Manuf. Technol., vol. 27, no. 1/2, pp. 205-210, Nov. 2005.

[54]

W. Lu, W. Wang, A. Y. T. Leung, S.-M. Lo, R. K. K. Yuen, Z. Xu, and H. Fan, "Air pollutant parameter forecasting using support vector machines," in Proc. 2002 Int. Joint Conf. on Neural Networks (IJCNN '02), May 12-17, 2002, vol. 1, pp. 630-635.

[55]

T. B. Trafalis, B. Santosa, and M. B. Richman, "Prediction of rainfall from WSR- 88D radar using kernel-based methods," Int. J. Smart Eng. Syst. Design, vol. 5, no. 4, pp. 429-438, Oct.-Dec. 2003.

[56]

W. Wang, Z. Xu, and J. W. Lu, "Three improved neural network models for air quality forecasting," Eng. Computat., vol. 20, no. 2, pp. 192-210, 2003.

[57]

H. Prem and N. R. Srinivasa Raghavan, "A support vector machine based approach for forecasting of network weather services," J. Grid Computing, vol. 4, no. 1, pp. 89-114, Mar. 2006.

[58]

M.-W. Chang, B.-J. Chen, and C.-J. Lin, "EUNITE network competition: Electricity load forecasting," Nov. 2001.

[59]

M. Mohandes, "Support vector machines for short-term load forecasting," Int. J. Energy Res., vol. 26, no. 4, pp. 335-345, Mar. 2002.

[60]

D. C. Sansom and T. K. Saha, "Energy constrained generation dispatch based on price forecasts including expected values and risk," in Proc. IEEE Power Energy Society General Meeting 2004, June 6-10, 2004, vol. 1, pp. 261-266.

[61]

L. Tian and A. Noore, "A novel approach for short-term load forecasting using support vector machines," Int. J. Neural Syst., vol. 14, no. 5, pp. 329-335, Aug. 2004.

[62]

B.-J. Chen, M.-W. Chang, and C.-J. Lin, "Load forecasting using support vector machines: A study on EUNITE competition 2001," IEEE Trans. Power Syst., vol. 19, no. 4, pp. 1821-1830, Nov. 2004.

[63]

B. Dong, C. Cao, and S. E. Lee, "Applying support vector machines to predict building energy consumption in tropical region," Energy Buildings, vol. 37, no. 5, pp. 545-553, May 2005.

[64]

Z. Bao, D. Pi, and Y. Sun, "Short term load forecasting based on self-organizing map and support vector machine," Lect. Notes Comput. Sci., vol. 3610, pp. 688-691, 2005.

Digital Library

[65]

P.-F. Pai and W. C. Hong, "Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms," Electric Power Syst. Res., vol. 74, no. 3, pp. 417-425, 2005.

[66]

Y. Ji, J. Hao, N. Reyhani, and A. Lendasse, "Direct and recursive prediction of time series using mutual information selection," in Proc. IWAAN 2005, June 8-10, 2005, pp. 1010-1017.

[67]

M.-G. Zhang, "Short-term load forecasting based on support vector machine regression, in Proc. 4th Int. Conf. on Machine Learning and Cybernetics, Aug. 18-21, 2005, vol. 7, pp. 4310-4314.

[68]

X. Li, C. Sun, and D. Gong, "Application of support vector machine and similar day method for load forecasting," Lect. Notes Comput. Sci., vol. 3611, pp. 602-609, 2005.

Digital Library

[69]

P.-F. Pai and W.-C. Hong, "Support vector machines with simulated annealing algorithms in electricity load forecasting," Energy Conv. Manage., vol. 46, no. 17, pp. 2669- 2688, Oct. 2005.

[70]

H.-S. Wu and S. Zhang, "Power load forecasting with least squares support vector machines and chaos theory," in Proc. Int. Conf. on Neural Networks and Brain, Oct. 13-15, 2005, vol. 2, pp. 1020-1024.

[71]

M. Espinoza, J. A. K. Suykens, and B. De Moor, "Load forecasting using fixed-size least squares support vector machines," Lect. Notes Comput. Sci., vol. 3512, pp. 1018-1026, 2005.

Digital Library

[72]

C.-C. Hsu, C.-H. Wu, S.-C. Chen, and K.-L. Peng, "Dynamically optimizing parameters in support vector regression: An application of electricity load forecasting," in Proc. 39th Annu. Hawaii Int. Conf. on System Sciences (HICSS 2006), Jan. 4-7, 2006, vol. 2, pp. 1-8.

[73]

M. Espinoza, J. A. K. Suykens, and B. De Moor, "Fixed-size least square support vector machines: A large scale application in electrical load forecasting," Computat. Manage. Sci., vol. 3, no. 2, pp. 113-129, Apr. 2006.

[74]

Y. He, Y. Zhu, and D. Duan, "Research on hybrid ARIMA and support vector machine model in short term load forecasting," in Proc. Int. Conf. on Intelligent Systems Design and Applications (ISDA '06), Oct. 2006, vol. 1, pp. 804-809.

[75]

J. Yang and Y. Zhang, "Application research of support vector machines in condition trend prediction of mechanical equipment," in Proc. 2nd Int. Symp. on Neural Networks (ISNN 2005), Lecture Notes in Computer Science, May 30-June 1, 2005, vol. 3498, pp. 857-864.

[76]

W.-C. Hong, P.-F. Pai, C.-T. Chen, and P.-T. Chang, "Recurrent support vector machines in reliability prediction," Lect. Notes Comput. Sci., vol. 3610, pp. 619-629, 2005.

Digital Library

[77]

W.-C. Hong and P.-F. Pai, "Predicting engine reliability using support vector machines," Int. J. Adv. Manuf. Technol., vol. 28, no. 1/2, pp. 154-161, Feb. 2006.

[78]

J. A. K. Suykens, J. Vandewalle, and B. De Moor, "Optimal control by least squares support vector machines," Neural Netw., vol. 14, no. 1, pp. 23-35, 2001.

Digital Library

[79]

S. Gezici, H. Kobayashi, and H. V. Poor, "A new approach to mobile position tracking," in Proc. IEEE Sarnoff Symp. on Advances in Wired and Wireless Communications, Mar. 11-12, 2003, pp. 204-207.

[80]

C.-J. Huang and C.-L. Cheng, "Application of support vector machines to admission control for proportional differentiated services enabled internet servers," in 2004 Proc. Int. Conf. on Hybrid Intelligent Systems, Dec. 5-8, 2004, pp. 248-253.

[81]

X. Liu, J. Yi, and D. Zhao, "Adaptive inverse disturbance cancelling control system based on least square support vector machines," in Proc. 2005 American Control Conf., June 8-10, 2005, pp. 2625-2629.

[82]

Q. Yang and S. Xie, "An application of support vector regression on narrow-band interference suppression in spread spectrum systems," Lect. Notes Comput. Sci., vol. 3611, pp. 442-450, Aug. 2005.

Digital Library

[83]

M. Martínez Ramón, N. Xu, and C. G. Christodoulou, "Beamforming using support vector machines," IEEE Antennas Wireless Propagat. Lett., vol. 4, pp. 439-442, 2005.

[84]

F. Luo, Y.-G. Xu, and J.-Z. Cao, "Elevator traffic f low prediction with least squares support vector machines," in Proc. 4th Int. Conf. on Machine Learning and Cybernetics, Aug. 18-21, 2005, pp. 4266-4270.

[85]

G. Xu, W. Tian, and Z. Jin, "An AGO-SVM drift modeling method for a dynamically tuned gyroscope," Measure. Sci. Technol., vol. 17, no. 1, pp. 161-167, Jan. 2006.

[86]

T. Frontzek, T. N. Lal, and R. Eckmiller, "Learning and prediction of the nonlinear dynamics of biological neurons with support vector machines," in Proc. Int. Conf. on Artificial Neural Networks (ICANN 2001), Lecture Notes in Computer Science, 2001, vol. 2130, pp. 390-398.

[87]

L. Ralaivola and F. d'Alche-Buc, "Dynamical modeling with kernels for nonlinear time series prediction," in Proc. Neural Information Processing Systems (NIPS 2003), Dec. 8-13, 2003.

[88]

L. Ralaivola and F. d'Alche-Buc, "Nonlinear time series filtering, smoothing, and learning using the kernel Kalman filter," Universite Pierre et Marie Curie, Paris, France, Tech. Rep., 2004.

[89]

M.-W. Chang, C.-J. Lin, and R. C.-H. Weng, "Analysis of switching dynamics with competing support vector machines," IEEE Trans. Neural Netw., vol. 15, no. 3, pp. 720-727, May 2004.

Digital Library

[90]

H. Liu, D. Liu, G. Zheng, and Y. Liang, "Research on natural gas load forecasting based on support vector regression," in Proc. 5th World Congress on Intelligent Control and Automation, June 15-19, 2004, pp. 3591-3595.

[91]

H. Liu, D. Liu, Y.-M. Liang, and G. Zheng, "Research on natural gas load forecasting based on least squares support vector machine," in Proc. 3rd Int. Conf. on Machine Learning and Cybernetics, Aug. 26-29, 2004, pp. 3124-3128.

[92]

C.-H. Wu, J.-M. Ho, and D. T. Lee, "Travel-time prediction with support vector regression," IEEE Trans. Intell. Transport. Syst., vol. 5, no. 4, pp. 276-281, Dec. 2004.

[93]

C. Zhang and H. Hu, "Using PSO algorithm to evolve an optimum input subset for a SVM in time series forecasting," in Proc. 2005 IEEE Conf. on Systems, Man, and Cybernetics, Oct. 10-12, 2005, vol. 4, pp. 3793-3796.

[94]

Support Vector Machines. {Online}. Available: http://www.support-vector.net/ index.html

[95]

Kernel Machines. {Online}. Available: http://www.kernel-machines.org/index. html

[96]

International Neural Network Society. {Online}. Available: http://www.inns.org/

[97]

Neural Computation. {Online}. Available: http://neco.mitpress.org/

[98]

European Neural Network Society. {Online}. Available: http://www.snn.ru.nl/ enns/

[99]

Asia Pacific Neural Network Assembly. {Online}. Available: http://www.apnna.net/ apnna

[100]

Japanese Neural Network Society. {Online}. Available: http://www.jnns.org/

[101]

Journal of Artificial Intelligence Research. {Online}. Available: http://www.jair. org/

[102]

S. Rüping. {Online}. Available: http://www-ai.cs.uni-dortmund.de/SOFTWARE/MYSVM/index.html

[103]

R. Collobert and S. Bengio, "SVMTorch: Support vector machines for large-scale regression problems," J. Machine Learning Res., vol. 1, pp. 143-160, Sept. 2001.

Digital Library

[104]

K. Pelckmans, J. A. K. Suykens, T. Van Gestel, J. De Brabanter, L. Lukas, B. Hamers, B. De Moor, and J. Vandewalle. {Online}. Available: http://www.esat.kuleuven.be/ sista/lssvmlab/tutorial/lssvmlab_paper0.pdf

[105]

M. E. Tipping, S. A. Solla, T. K. Leen, and K.-R. Müller, "The relevance vector machine," in Advances in Neural Information Processing Systems 12. Cambridge, MA: MIT Press, 2000.

Cited By

Andrade PSilva IDiniz MFlores TCosta DSoares E(2024)Online Processing of Vehicular Data on the Edge Through an Unsupervised TinyML Regression TechniqueACM Transactions on Embedded Computing Systems10.1145/359135623:3(1-28)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3591356
Gong YHe TChen MWang BNie LYin Y(2024)Spatio-Temporal Enhanced Contrastive and Contextual Learning for Weather ForecastingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336282536:8(4260-4274)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3362825
Hurtado SAntequera-Gómez MBarba-González CPicornell ANavas-Delgado I(2024)e-Science workflowKnowledge-Based Systems10.1016/j.knosys.2023.111230284:COnline publication date: 25-Jan-2024
https://dl.acm.org/doi/10.1016/j.knosys.2023.111230
Show More Cited By

Time series prediction using support vector machines: a survey
1. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms

Recommendations

Iterated time series prediction with multiple support vector regression models

Support vector regression (SVR) model has been widely applied to time series prediction. In general iterative methods, the multi-step-ahead prediction is based on the iteration of the exact one-step prediction. Even if the one-step prediction model is ...
Wavelet twin support vector machines based on glowworm swarm optimization

Twin support vector machine is a machine learning algorithm developing from standard support vector machine. The performance of twin support vector machine is always better than support vector machine on datasets that have cross regions. Recently ...
Incremental training of support vector machines using hyperspheres

In the conventional incremental training of support vector machines, candidates for support vectors tend to be deleted if the separating hyperplane rotates as the training data are added. To solve this problem, in this paper, we propose an incremental ...

Comments

Information & Contributors

Information

Published In

cover image IEEE Computational Intelligence Magazine

IEEE Computational Intelligence Magazine Volume 4, Issue 2

May 2009

53 pages

ISSN:1556-603X

Issue’s Table of Contents

Copyright © 2009.

Publisher

IEEE Press

Publication History

Published: 01 May 2009

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

148
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Andrade PSilva IDiniz MFlores TCosta DSoares E(2024)Online Processing of Vehicular Data on the Edge Through an Unsupervised TinyML Regression TechniqueACM Transactions on Embedded Computing Systems10.1145/359135623:3(1-28)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3591356
Gong YHe TChen MWang BNie LYin Y(2024)Spatio-Temporal Enhanced Contrastive and Contextual Learning for Weather ForecastingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336282536:8(4260-4274)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3362825
Hurtado SAntequera-Gómez MBarba-González CPicornell ANavas-Delgado I(2024)e-Science workflowKnowledge-Based Systems10.1016/j.knosys.2023.111230284:COnline publication date: 25-Jan-2024
https://dl.acm.org/doi/10.1016/j.knosys.2023.111230
Qin LGu HWei WXiao ZLin ZLiu LWang N(2024)Spatio-temporal communication network traffic prediction method based on graph neural networkInformation Sciences: an International Journal10.1016/j.ins.2024.121003679:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.121003
Wu GHu YChiu YJiang PChi R(2024)Forecast combination using grey relational analysis and Choquet fuzzy integral for container throughput forecastingExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.124170252:PAOnline publication date: 24-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.124170
Huo DChen JWang T(2024)Chaos-based support vector regression for load power forecasting of excavatorsExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123169246:COnline publication date: 15-Jul-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123169
Li TGuo ZLi QWu Z(2024)Multi-scale deep echo state network for time series predictionNeural Computing and Applications10.1007/s00521-024-09761-436:21(13305-13325)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1007/s00521-024-09761-4
Shen LKwok JKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Non-autoregressive conditional diffusion models for time series predictionProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619692(31016-31029)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619692
Chen SLong GShen TJiang JElkind E(2023)Prompt federated learning for weather forecastingProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/393(3532-3540)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/393
Li FZhang ZChu XZhang JQiu SZhang J(2023)A Meta-Learning Based Framework for Cell-Level Mobile Network Traffic PredictionIEEE Transactions on Wireless Communications10.1109/TWC.2023.324724122:6(4264-4280)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1109/TWC.2023.3247241
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents