Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Cluster Computing
Published in: Cluster Computing 2/2019

20-02-2018

Decision making based on grey model and support vector machine

Authors: Li Futou, Liu Liang

Published in: Cluster Computing | Special Issue 2/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

In order to improve the effectiveness of market preference early warning analysis algorithm, a new method based on gray kernel AR- SVM model is proposed. Firstly, we used support vector machine (SVM) algorithm to construct the financial market risk warning analysis model, which includes no extreme risk and extreme risk in two cases, and used SVM algorithm to find the optimal classification process based on the training set; Secondly, the SVM model is prone to extreme risk warning “failure” in the market preference prediction problem in the market preference data records are processed by the improved gray model, and the mixed kernel function was used to improve the SVM algorithm, which realized the sample data to improve the prediction performance of autoregressive model. Finally, the SVM algorithm is used to improve the accuracy of the market model. The experimental results show that the proposed method is effective in the analysis of market preference data.
previous article Research on multichannel transmission of security information in MAC layer based on zoom and forward in VANETs
next article Cellular planning for next generation wireless mobile network using novel energy efficient CoMP

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Anand, S., Chandramouli, R.M.: A micro-finance model for spectrum management in wireless networks. IEEE Trans. Cogn. Commun. Netw. 2(3), 263–272 (2016)
2.
Varma, J.R., Virmani, V.: Computational finance using QuantLib-Python. Comput. Sci. Eng. 18(2), 78–88 (2016)
3.
Burr, M.T., Zimmer, M.J., Warner, G., et al.: Emerging models for microgrid finance: driven by the need to deliver value to end users. IEEE Electrification Mag. 2(1), 30–39 (2014)
4.
Shiao, H.-T., Cherkassky, V., Lee, J., et al.: SVM-based system for prediction of epileptic seizures from iEEG signal. IEEE Trans. Biomed. Eng. 64(5), 1011–1022 (2017)
5.
Singh, D., Roy, D., Mohan, C.K.: DiP-SVM: distribution preserving kernel support vector machine for big data. IEEE Trans. Big Data 3(1), 79–90 (2017)
6.
Sukawattanavijit, C., Chen, J., Zhang, H.: GA-SVM algorithm for improving land-cover classification using SAR and optical remote sensing data. IEEE Geosci. Remote Sens. Lett. 14(3), 284–288 (2017)
7.
Reis, P.M.G.I., da Costa, J.P.C.L., Miranda, R.K., et al.: ESPRIT-Hilbert-based audio tampering detection with SVM classifier for forensic analysis via electrical network frequency. IEEE Trans. Inf. Forensics Secur. 12(4), 853–864 (2017)
8.
Park, S., Lee, H.S., Kim, J.: Seed growing for interactive image segmentation using SVM classification with geodesic distance. Electron. Lett. 53(1), 22–24 (2017)
9.
Arunkumar, N., Ramkumar, K., Venkatraman, V., Abdulhay, E., Fernandes, S.L., Kadry, S., Segal, S.: Classification of focal and non focal EEG using entropies. Pattern Recognit. Lett. 94, 112–117 (2017)
10.
Arunkumar, N., Kumar, K.R., Venkataraman, V.: Automatic detection of epileptic seizures using new entropy measures. J. Med. Imaging Health Inform. 6(3), 724–730 (2016)
11.
Arunkumar, N., Ram Kumar, K., Venkataraman, V.: Automatic detection of epileptic seizures using permutation entropy, tsallis entropy and kolmogorov complexity. J. Med. Imaging Health Inform. 6(2), 526–531 (2016)
12.
Wu, Y., Yang, X., Plaza, A., et al.: Approximate computing of remotely sensed data: SVM hyperspectral image classification as a case study. IEEE J Sel. Top. Appl. Earth Obs. Remote Sens. 9(12), 5806–5818 (2016)
13.
Sriwastava, B.K., Basu, S., Maulik, U.: Predicting protein-protein interaction sites with a novel membership based fuzzy SVM classifier. IEEE/ACM Trans. Comput. Biol. Bioinf. 12(6), 1394–1404 (2015)
14.
Ai, Y., Shao, X., Jiang, P., et al.: Erratum to: process modeling and parameter optimization using radial basis function neural network and genetic algorithm for laser welding of dissimilar materials. Appl. Phys. A 121(3), 1317–1318 (2015)
15.
Xu, S.J., Han, J.Q., Liu, G.H., et al.: The single best artificial neural network and neural network ensemble in modeling of palladium microextraction. Monatshefte für Chemie (Chem Mon.) 146(8), 1217–1227 (2015)
16.
Zhang, M., Tang, J., Zhang, X., et al.: Intelligent diagnosis of short hydraulic signal based on improved EEMD and SVM with few low-dimensional training samples. Chin. J. Mech. Eng. 29(2), 396–405 (2016)MathSciNet
17.
Shamshirb, S., Mohammadi, K., Tong, C.W., et al.: Ahybrid SVM-FFA method for prediction of monthly mean global solar radiation. Theor. Appl. Climatol. 125(1), 53–65 (2016)
Metadata
Title
Decision making based on grey model and support vector machine
Authors
Li Futou
Liu Liang
Publication date
20-02-2018
Publisher
Springer US
Published in
Cluster Computing / Issue Special Issue 2/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-018-2227-7

Other articles of this Special Issue 2/2019

Cluster Computing 2/2019 Go to the issue

OriginalPaper

Theoretical and empirical research on investment behavior strategy based on fuzzy probability

OriginalPaper

Research on religious management based on big data

OriginalPaper

A location algorithm based on Stiefel manifold particle filtering

OriginalPaper

A trust based security framework with anonymous authentication system using multiple attributes in decentralized cloud

OriginalPaper

Soft computing approaches based bookmark selection and clustering techniques for social tagging systems

OriginalPaper

Discrimination method of wire rope fault signal based on Holzer sensor for multi array weak magnetic detection

Premium Partner

    Image Credits