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
International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 12/2019

25-01-2019 | Original Article

Sensitive time series prediction using extreme learning machine

Authors: Hong-Bo Wang, Xi Liu, Peng Song, Xu-Yan Tu

Published in: International Journal of Machine Learning and Cybernetics | Issue 12/2019

Log in

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

search-config
loading …

Abstract

Inspired by a multi-granularity and fractal theory, this work mainly focuses on how to conceive a training and test dataset at different levels under a small dataset in a complex real-time application. Such applications do not purely pursue most accurate values, but a low-cost(sub-optimal) solution may be popular during a timely prediction on those sensitive time series. Then a chaotic system is experimented and analysed in detail for three gap-sampling schemes, namely, microscope, middle scale and macro scope. At the same time, the influence of different activation functions on the accuracy and speed of their network model is discussed. The efficiency of sensitive time series using Extreme Learning Machine (ST-ELM) is examined on six widely used datasets (Abalone, Auto-MPG, Body fat, California Housing, Cloud and Strike). The simulations show that the suggested ST-ELM can improve the existing performance when dealing with the idle spectrum prediction of cognitive wireless network.
previous article A fast decision making method for mandatory lane change using kernel extreme learning machine
next article Automatically synthesizing DoS attack traces using generative adversarial networks

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
Show more products
Literature
1.
Shi ZW, Han M (2007) Support vector echo-state machine for chaotic time-series prediction. IEEE Trans Neural Netw 18(2):359–372CrossRef
2.
Wong PK, Wong HC, Vong CM (2012) Online time-sequence incremental and decremental least squares support vector machines for engine air-ratio prediction. Int J Engine Res 13(1):28–40CrossRef
3.
Lv Y, Duan Y, Kang W, Li Z, Wang FY (2015) Traffic flow prediction with big data: a deep learning approach. IEEE Trans Intell Transp Syst 16(2):865–873
4.
Islam S, Talukdar B (2016) Performance Improvement of a Rainfall Prediction Model using Particle Swarm Optimization. Int J Comput Eng Res 6(7):39–42
5.
Piotrowski AP, Napiorkowski JJ, Osuch M, Napiorkowski MJ (2016) On the importance of training methods and ensemble aggregation for run-off prediction by means of artificial neural networks. Hydrol Sci J 61(10):1903–1925
6.
Li M-B, Huang G-B, Saratchandran P (2005) Fully complex extreme learning machine. Neurocomputing 68(10):306–314CrossRef
7.
Feng G, Huang G-B, Lin Q (2009) Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Trans Neural Netw 20(8):1352–1357CrossRef
8.
Rong H-J, Ong Y-S, Tan A-H (2008) A fast pruned-extreme learning machine for classification problem. Neurocomputing 72(1):359–366CrossRef
9.
Miche Y, Sorjamaa A, Bas P (2010) OP-ELM: optimally pruned extreme learning machine. IEEE Trans Neural Netw 21(1):158–162CrossRef
10.
Lan Y, Soh YC, Huang G-B (2010) Constructive hidden nodes selection of extreme learning machine for regression. Neurocomputing 73(16):3191–3199CrossRef
11.
Huang G-B, Chen L, Siew CK (2006) Universal approximation using incremental constructive feed forward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879–892CrossRef
12.
Huang G, Liang N, Rong H, Saratchandran P, Sundararajan N (2005) On-line sequential extreme learning machine, the IASTED international conference on computational intelligence(CI2005),Canada, July, pp. 4-6
13.
Liang N, Huang G, Saratchandran P, Sundararajan N (2006) A fast and accurate online sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17:1411–1423CrossRef
14.
Vong Chi Man, Tai Keng Iam, Pun Chi Man, Wong Pak Kin (2015) Fast and accurate face detection by sparse Bayesian extreme learning machine. Neural Comput Appl 26(5):1149–1156CrossRef
15.
Arrachman1 Samudra R, Adiatmoko MF, Soeprijanto Adi, Syai’in Mat, Sidik MSA, Rohiem NH (2017) Smart Meter based on Time Series Modify and Extreme Learning Machine. In: 2017 2nd international conference on automation, cognitive science, optics, micro electro-mechanical system, and information technology (ICACOMIT), October 23, 2017, Jakarta, Indonesia, pp 86-92
16.
Vong Chi-Man, Ip Weng-Fai, Chiu Chi-Chong, Wong Pak-Kin (2015) Imbalanced learning for air pollution by meta-cognitive online sequential extreme learning machine. Cogn Comput 7(3):381–391CrossRef
17.
Jie Du, Vong Chi-Man, Pun Chi-Man, Wong Pak-Kin, Ip Weng-Fai (2017) Post-boosting of classification boundary for imbalanced data using geometric mean. Neural Netw 96(12):101–114
18.
Wong Pak Kin, Gao Xiang Hui, Wong Ka In, ManVong Chi (2018) Efficient point-by-point engine calibration using machine learning and sequential design of experiment strategies. J Frankl Inst 355(4):1517–1538MathSciNetMATHCrossRef
19.
Wong Pak Kin, Gao Xiang Hui, Wong Ka In, ManVong Chi (2018) Online extreme learning machine based modeling and optimization for point-by-point engine calibration. Neurocomputing 277(2):187–197CrossRef
20.
21.
Park J-M, Kim J-H (2017) Online recurrent extreme learning machine and its application to time-series prediction. In: 2017 International joint conference on neural networks (IJCNN), Anchorage, AK,pp.1983-1990
22.
Boeing G (2016) Visual analysis of nonlinear dynamical systems: chaos, fractals, self-similarity and the limits of prediction. Systems 4(4):37CrossRef
23.
Shon SH, Jang SJ, Kim JM (2010) HMM-based adaptive frequency-hopping cognitive radio system to reduce interference time and to improve throughput. KSII Trans Internet Inf Syst 4(4):475–490
24.
Bütün İ, Talay A ç, Altilar DT, Khalid M, Sankar R (2010) Impact of mobility prediction on the performance of Cognitive Radio networks. In: 2010 wireless telecommunications symposium (WTS), Tampa, FL, 2010, pp. 1-5
25.
Wen Z, Luo T, Xiang W (2008) Autoregressive spectrum hole prediction model for cognitive radio systems, ICC Workshops - 2008 IEEE International Conference on Communications Workshops, Beijing, 2008, pp.154-157
26.
Geirhofer S, Tong L, Sadler BM (2008) Cognitive medium access: constraining interference based on experimental models. IEEE J Sel Areas Commun 26(1):475–490CrossRef
27.
Wang X, Han M (2015) Improved extreme learning machine for multivariate time series online sequential prediction. Eng Appl Artif Intell 40(4):28–36CrossRef
28.
Zhang R, Xu M, Han M, et al. (2017) Multivariate chaotic time series prediction based on improved extreme learning machine. In: 36th Chinese control conference (CCC), Dalian, 2017, pp 4006-4011
29.
Chen Chuangquan, Vong Chi-Man, Wong Chi-Man, Wang Weiru, Wong Pak-Kin (2018) Efficient extreme learning machine via very sparse random projection. Soft Comput 22(11):3563–3574MATHCrossRef
30.
Lian C, Zeng ZG, Yao W, Tang HM (2013) Ensemble of extreme learning machine for land slide displacement prediction based on time series analysis. Neural Comput Appl 24(1):99–107CrossRef
31.
Salcedo-Sanz S, Casanova-Mateo C, Pastor-Snchez A, Snchez Girn M (2014) Daily global solar radiation prediction based on a hybrid coral reefs optimization extreme learning machine approach. Solar Energy 105(2014):91–98CrossRef
32.
Fernndez-Delgado M, Cernadas E, Barro S, Ribeiro J, Neves J (2014) Direct Kernel Perceptron (DKP): ultra-fast kernel ELM-based classification with non-iterative closed-form weight calculation. Neural Netw 50(2):60–71MATHCrossRef
33.
Wong CM, Vong CM, Wong PK, Cao J (2018) Kernel-based multilayer extreme learning machines for representation learning. IEEE Trans Neural Netw Learn Syst 29(3):757–762MathSciNetCrossRef
34.
Vong Chi-Man, Chen Chuangquan, Wong Pak-Kin (2018) Empirical kernel map-based multilayer extreme learning machines for representation learning. Neurocomputing 318(10):265–276CrossRef
35.
Canzian Luca, Zhang Yu, Schaar Mihaela vander (2015) Ensemble of distributed learners for online classification of dynamic data streams. IEEE Trans Signal Inf Process Over Netw 1(3):180–194MathSciNetCrossRef
36.
Guo X, Pang Y, Yan G, et al. (2017) Time series forecasting based on deep extreme learning machine. In: 29th Chinese control and decision conference (CCDC), Chongqing, 2017, pp 6151-6156
37.
Adhikari R, Agrawal RK (2013) A homogeneous ensemble of artificial neural networks for time series forecasting. Int J Comput Appl 32(7):1–8
38.
Bodnar Olha, Schmid Wolfgang (2017) CUSUM control schemes for monitoring the covariance matrix of multivariate time series. Statistics 51(4):722–744MathSciNetMATHCrossRef
39.
Lin L, Wang F, Xie X (2017) Random forests-based extreme learning machine ensemble for multi-regime time series prediction. Expert Syst Appl 83(C):164–176CrossRef
40.
Ding S, Zhao H, Zhang Y, Xu X, Nie R (2015) Extreme learning machine: algorithm, theory and applications. Artif Intell Rev 44(1):103–115CrossRef
41.
Huang G-B, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multi-class classification. IEEE Trans Syst Man Cybern Part B (Cybernetics) 42(2):513–529CrossRef
42.
Heidari-Bateni G, McGillem CD (1994) A chaotic direct-sequence spread-spectrum communication system. IEEE Trans Commun 42(234):1524–1527CrossRef
43.
Sedaghatnejad S, Farhang M (2015) Detectability of chaotic direct-sequence spread-spectrum signals. IEEE Wirel Commun Lett 4(6):589–592CrossRef
44.
Zhao Pengfei, Xing Lei, Jun Yu (2009) Chaotic time series prediction: from one to another. Phys Lett A 373(25):2174–2177MATHCrossRef
45.
Kasun LLC, Zhou H, Huang G-B, Vong CM (2013) Representational learning with extreme learning machine for big data. IEEE Intell Syst 28(6):31–34
46.
Tang J, Deng C, Huang G-B (2016) Extreme learning machine for multilayer perceptron, IEEE Trans. Neural Netw Learn Syst 27(4):809–821MathSciNetCrossRef
Metadata
Title
Sensitive time series prediction using extreme learning machine
Authors
Hong-Bo Wang
Xi Liu
Peng Song
Xu-Yan Tu
Publication date
25-01-2019
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 12/2019
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-019-00924-7

Other articles of this Issue 12/2019

International Journal of Machine Learning and Cybernetics 12/2019 Go to the issue

Original Article

Selective multi-descriptor fusion for face identification

Original Article

A fast decision making method for mandatory lane change using kernel extreme learning machine

Original Article

A novel decomposition-ensemble approach to crude oil price forecasting with evolution clustering and combined model

Original Article

Binary multi-verse optimization algorithm for global optimization and discrete problems

Original Article

Fast and unsupervised outlier removal by recurrent adaptive reconstruction extreme learning machine

Original Article

Meticulous fuzzy convolution C means for optimized big data analytics: adaptation towards deep learning