A feature fusion based forecasting model for financial time series

PLoS ONE

Volumn 9, Issue 6, 2014, Pages

  Guo, Zhiqiang ^a   Wang, Huaiqing ^b   Liu, Quan ^a   Yang, Jie ^a  

^a WUHAN UNIVERSITY OF TECHNOLOGY   (China)

^b SOUTHERN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CORRELATION ANALYSIS; FINANCIAL INFORMATION SYSTEM; FORECASTING; INDEPENDENT COMPONENT ANALYSIS; INFORMATION PROCESSING; MARKET; PREDICTION; STOCK MARKET; SUPPORT VECTOR MACHINE; TIME SERIES ANALYSIS; ECONOMICS; INVESTMENT; STATISTICAL MODEL; TRENDS;

INVESTMENTS; MODELS, ECONOMIC;

EID: 84903398919     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0101113     Document Type: Article

References (69)

1. Felsen J (1975) Learning pattern recognition techniques applied to stock market forecasting. IEEE T SYS MAN CY 5(6):583-594
2. Verleysen M, François D (2005) The curse of dimensionality in data mining and time series prediction.In Proceeding of the 18th International Conference on Artificial Neural Networks:computational Intelligence and Bioinspired Systems, Springer-Verlag Berlin, Heidelberg, pp 758-770.
3. Ince H, Trafalis T (2007) Kernel principal component analysis and support vector machines for stock price prediction. IIE Transactions 39(6):629-637. (Pubitemid 46502165)
4. Fu T, Chung F, Ng V, Luk R (2001) Pattern discovery from stock time series using self-organizing maps. In Workshop Notes of KDD2001 Workshop on Temporal Data Mining, pp 27-37.
5. Keogh E, Chakrabati K, Pazzani M, Mehrota S (2000) Dimensionality reduction for fast similarity search in large time series databases. Knowledge and Information Systems 3(3): 263-286.
6. Bunau PV, Meinecke FC, Kiraly FC, Muller KR (2009) Finding stationary subspaces in multivariate time series Physical review letters 103(21): 214101.
7. Batal I,Hauskrecht M (2009) A supervised time series feature extraction technique using DCT and DWT. In International Conference on Machine Learning and Applications, Miami Beach, Florida, pp 735-739.
8. Perng CS, Wang H, Zhang SR, Parker DS (2000) Landmarks: A new model for similarity-based pattern querying in time series databases. In 16 International Conference on Data Engineering, IEEE ICDE, pp 33-42.
9. Laloux L, Cizeau P, Bouchaud JP, Potters M (1999) Noise dressing of financial correlation matrices, Phys. Rev. Lett. 83(7):1467-1470. (Pubitemid 129690137)
10. Zhao Y, Zhang S (2006) Generalized dimension-reduction framework for recent-biased time series analysis. IEEE Trans Knowl Data Eng 18(2):231-244.
11. White H (1988) Economic prediction using neural networks: A case of IBM daily stock returns. In IEEE International Conference on Neural Networks, pp 451-458.
12. Sutheebanjard P, Premchaiswadi W (2010). Stock exchange of Thailand index prediction using back propagation neural networks. In 2nd International Conference on Computer and Network Technology. IEEE Computer Society Press, Washington, pp 377-380.
13. Liang J, Song W, Wang M (2011) Stock market prediction based on procedural neural networks. Advances in Artificial Neural Systems doi:10.1155/2011/814769.
14. Chen AS, Leung MT, Daouk H (2003) Application of neural networks to an emerging financial market: Forecasting and trading the Taiwan Stock Index. Comput Oper Res 30(6):901-923. (Pubitemid 36175299)
15. Padhiary PK, Mishra AP (2011) Development of improved artificial neural network model for stock market prediction. Int J Eng Sci 3(2):1576-1581.
16. Hsieh TJ, Hsiao HF, Yeh WC (2011). Forecasting stock markets using wavelet transforms and recurrent neural networks: An integrated system based on artificial bee colony algorithm. Appl Soft Comput 11(2):2510-2525.
17. Parras-Gutierrez E, Garcia-Arenas M, Rivas VM, Jesus MJ (2012). Coevolution of lags and RBFNs for time series forecasting: L-Co-R algorithm. Soft Comput 16(6):919-942.
18. Vapnik VN (1995) The Nature of Statistical L earning Theory, Springer-Verlag, New York.
19. Das SP, Padhy S (2012) Support vector machines for prediction of futures prices in Indian stock market. Int J Comput Appl 41(3):22-26.
20. Lu CJ, Lee TS, Chiu CC (2009) Financial time series forecasting using independent component analysis and support vector regression. Decision Support Systems 47 (2):115-125.
21. He W, Wang Z, Jiang H (2008) Model optimizing and feature selecting for support vector regression in time series forecasting. Neurocomputing 72 (1-3): 600-611.
22. Huang W, Nakamori Y, Wang SY (2005) Forecasting stock market movement direction with support vector machine. Comput Oper Res 32(10):2513-2522. (Pubitemid 40219758)
23. Cao LJ (2003) Support vector machines experts for time series forecasting, Neurocomputing (51): 321-339.
24. Huang SC, Li CC, Lee CW, Chang MJ (2010) Combining ICA with kernel based regressions for trading support systems on financial options. In The 3rd International Symposium on Intelligent Decision Technologies and Intelligent Interactive Multimedia Systems and Services. Baltimore, pp 163-169.
25. Guo C, Su M (2001) Spectral clustering method based on independent component analysis for time series. Systems Engineering Theory & Practice 31(10):1921-1931.
26. Yeh CC, Chang B, Lin HC (2009).Integrating phase space reconstruction, independent component analysis and random forest for financial time series forecasting. In The 29th Annual International Symposium on Forecasting, Hong Kong, pp 21-24.
27. Lu CJ, Wang YW (2010) Combining independent component analysis and growing hierarchical self-organizing maps with support vector regression in product demand forecasting. Int.J. Production Economics 128(2):603-613.
28. Samsudin R, Shabri A, Saad P (2010) A comparison of time series forecasting using support vector machine and artificial neural network model. Journal of Applied Sciences 10(11):950-958.
29. Wu JX, Wei JL (2007) Combining ICA with SVR for prediction of finance time Series. In IEEE International Conference on Automation and Logistics. Jinnan, pp 18-21.
30. Matteson DS, Tsay RS (2011). Dynamic orthogonal components for multivariate time series, J AM Stat Assoc 160 (496):1450-1463.
31. Ahn H, Choi E, Han I (2007) Extracting underlying meaningful features and canceling noise using independent component analysis for direct marketing. Expert Syst Appl 33(1):181-191. (Pubitemid 46001573)
32. Mok PY, Lam KP, NG HS (2004) An ICA design of intraday stock prediction models with automatic variable selection, IEEE International Joint Conference on Neural Networks, Vol 3,Budapest, pp 2135-2140.
33. Lizieri C, Satchell S, Zhang Q (2007) The underlying return-generating Factors for REIT Returns: an application of independent component analysis. Real Estate Economics 35(4): 569-598.
34. Lu CJ (2010). Integrating independent component analysis-based denoising scheme with neural network for stock price prediction. Expert Syst Appl.37(10):7056-7064.
35. Wu E, Yu P (2005) Volatility modelling of multivariate financial time series by using ICA-GARCH models. Intelligent Data Engineering and Automated Learning - IDEAL 2005 Lecture Notes in Computer Science (3578/2005): 179-187.
36. Cao LJ, Chong WK (2003) A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing 55(1-2):321-336. (Pubitemid 37336682)
37. Hyvarinen A, Oja E (2000) Independent component analysis: algorithms and applications. Neural Networks. 13(4-5):411-430. (Pubitemid 30447427)
38. Cardoso J (1989) Source separation using higher order moments, International Conference on Acoustics, Speech and Signal Processing, Vol 4, Glasgow, pp 2109-2112.
39. Cardoso J, Souloumiac A (1993) Blind beamforming for non-Gaussian signals. Radar and Signal Processing, IEE Proceedings F, 140(6): 771-774.
40. Bel A, Sejnowski T (1995). An information maximization approach to blind separation and blind deconvolution. Neural Computation. 7(6):1129-1159.
41. Amari S, Cichocki A, Yang H (1996) A new learning algorithm for blind signal separation, in Advances in Neural Information Processing Systems 8, Editors D. Tesauro, M. Mozer and M . Hasselmo, MIT Press, Cambridge, pp 757-763.
42. Hyvarinen A (1996) A Simple one-unit algorithms for blind source separation and blind deconvolution,In Proceeding of International Conference on Neural Information Processing, Vol 2, Hong Kong, pp 1201-1206.
43. Bartlett MS, Movellan JR, Sejnowski TJ (2002), Face Recognition by Independent Component Analysis, IEEE Trans. on Neural Networks 13(6):1450-1464. (Pubitemid 35428531)
44. Yu L, Chen HH, Wang SY, Lai KK (2009) Evolving Least Squares Support Vector Machines for Stock Market Trend Mining. IEEE Transactions on evolutionary computation. 13(1):87-102.
45. Kim KJ (2003) Financial time series forecasting using support vector machines. Neurocomputing 55 (1-2): 307-319.
46. Sui XS, Qi ZY, Yu DR, Hu QH, Zhao H, et al (2007) A Novel Feature Selection Approach Using Classification Complexity for SVM of Stock Market Trend Prediction. International Conference on Management Science & Engineering (14th). Harbin, pp1654-1659.
47. Atsalakis GS, Valavanis KP (2009) Surveying stock market forecasting techniques - Part II: Soft computing methods. Expert Systems with Applications 36(3):5932-5941.
48. Teixeira LA, Oliveira ALI (2010) A method for automatic stock trading combining technical analysis and nearest neighbor classification.?Expert Syst Appl 37(10):6885-6890.
49. Huang W, Nakamori Y, Wang SY (2005) Forecasting stock market movement direction with support vector machine. Comput. Operat. Res. 32(10): 2513-2522. (Pubitemid 40219758)
50. Ettes D (2000) Trading the stock markets using genetic fuzzy modelling. In Proceedings of Conference on Computational Intelligence for Financial Engineering, pp 22-25.
51. Zorin A, Boriso A (2002) Modelling riga stock exchange index using neural networks. http://www.knolsearch.com/ebook/-F0eT3uE5qwfgttFnFZIS8do W2xnVMqs6sJfXBQ-UAU00XDpkOMQPg70XrJTtTAx9dvs3garSmzN- OvNYyTY0napVPSp6RTXA6Z3aq0Ntwrk-zl526VtGmqHZwFDRRh0ClOin1- mfNy92a3Gk9plNbtHbFnr10m-9-kvSUa50MB4/modelling-riga-stock-exchange-index-using- neural-networks-...-LASG.html.
52. Hall DL, Linas J (1997). An introduction to multisensory data fusion. Proceedings of the IEEE 85(1): 6-23.
53. Sun QS, Zeng SG, Liu Y, Heng PA, Xia DS, et al (2005). A new method of feature fusion and its application in image Recognition. Pattern Recognition 38(12):2437-2448. (Pubitemid 41336713)
54. Cui B, Anthony KH, Zhang TC, Zhao Z (2010). Multiple feature fusion for social media applications. Proceedings of the 2010 ACM SIGMOD. Indianapolis, pp 435-446.
55. Su SZ, Chen SY (2013) Analysis of feature fusion based on HIK SVM and its application for pedestrian detection. Abstract and Applied Analysis. Vol.2013, pp.1-11, Online: http://dx.doi.org/ doi:10.1155/2013/436062.
56. Cao LJ, Tay FEH (2003) Support vector machine with adaptive parameters in financial time series forecasting. IEEE Trans Neural Netw 14(6):1506-1518.
57. Tay FEH, Cao LJ (2002) Modified support vector machines in nancial time series forecasting. Neurocomputing 48(1-4):847-861. (Pubitemid 36221530)
58. Cowles A, Jones HE (1937) Some a posteriori probabilities in stock market action. The econometric society 5(3):280-294.
59. Dong Y, Yang BC (2006) The periodic analysis of realized volatility in china stock market. Journal of Tianjin University of Technology. 22(6):24-26.
60. Hotelling H (1936) Relations between two sets of variates, Biometrika.28(3-4):321-377.
61. Kai KA, Chai Q (2006) Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach. IEEE Trans Neural Netw 17(5):1301-1315.
62. th World Congress on Intelligent Control and Automation, Jinan, pp 5048-5053.
63. Saigal S, Meheotra D (2012) Performance comparison of time series data using predictive data mining techniques. Advances in Information Mining 4(1):57-66.
64. Achelis S (2013) Technical Analysis from A to Z, 2nd Edition. McGraw-Hill.
65. Plerou V, Gopikrishnan P, Rosenow B, Amaral LAN, Guhr T, et al (2002) A Random Matrix Theory Approach to Cross-Correlations in Financial Data, Phys. Rev. E 65 066126.
66. Hong ZQ, Yang JY (1999) Optimal discriminant plane for a small number of samples and design method of classifier on the plane. Pattern Recognition 24(4): 317-324.
67. Pinquier J, Karaman S, Letoupin L, Guyot P (2012) Strategies for multiple feature fusion with Hierarchical HMM: Application to activity recognition from wearable audiovisual sensors. 21st International Conference on Pattern Recognition (ICPR). Tsukuba, pp 3192-3195.
68. Lendasse A, Lee J, Bodt ÉD, Wertz V, Verleysen M, et al(2001) Dimension reduction of technical indicators for the prediction of financial time series - Application to the BEL20 Market Index. European Journal of Economic and Social Systems15No 2 (2001) 31-48.
69. Kwak N, Kim C, Kim H (2008) Dimensionality reduction based on ICA for regression problem. Neurocomputing 71(13-15):2596-2603.