Stock temporal prediction based on time series motifs

Recent researches pay more attention to stock tendency prediction, which various machine learning approaches have been proposed. In this paper, we propose an algorithm to discover self-correlation of stock price in virtue of the notion of time series motifs, by viewing stock price sequences as time series. Generally, time series motif is a pattern appearing frequently in a time sequence, useful to forecast the stock temporal tendencies and prices as a reliable part in time series. In the proposed approach, we firstly search for one part of time series motifs using ordinal comparison and k-NN clustering algorithm, and then attempt to discover the correlation between motifs and subsequences connected behind them. Experimental results demonstrate the positive contribution of time series motifs, the acceptable prediction accuracy, and priority of our algorithm.

[1]  Tohru Ikeguchi,et al.  Chaotic Motif Sampler for Motif Discovery Using Statistical Values of Spike Time-Series , 2007, ICONIP.

[2]  Yong Li,et al.  Frequent Patterns of Investment Behaviors in Shanghai Stock Market , 2008, 2008 International Conference on Computer Science and Software Engineering.

[3]  Anthony K. H. Tung,et al.  Efficient Mining of Intertransaction Association Rules , 2003, IEEE Trans. Knowl. Data Eng..

[4]  Jinchuan Ke,et al.  Empirical Analysis of Optimal Hidden Neurons in Neural Network Modeling for Stock Prediction , 2008, 2008 IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application.

[5]  Yi-Hsien Wang,et al.  Nonlinear neural network forecasting model for stock index option price: Hybrid GJR-GARCH approach , 2009, Expert Syst. Appl..

[6]  Shu-Hsien Liao,et al.  Mining stock category association and cluster on Taiwan stock market , 2008, Expert Syst. Appl..

[7]  Ritanjali Majhi,et al.  Efficient Prediction of Foreign Exchange Rate using Nonlinear Single Layer Artificial Neural Model , 2006, 2006 IEEE Conference on Cybernetics and Intelligent Systems.

[8]  Stephen Shaoyi Liao,et al.  Discovering original motifs with different lengths from time series , 2008, Knowl. Based Syst..

[9]  Ganapati Panda,et al.  Efficient prediction of exchange rates with low complexity artificial neural network models , 2009, Expert Syst. Appl..

[10]  Pei-Chann Chang,et al.  A neural network with a case based dynamic window for stock trading prediction , 2009, Expert Syst. Appl..

[11]  Yu Tang,et al.  Web-based fuzzy neural networks for stock prediction , 2002 .

[12]  Edward Tsang,et al.  EDDIE beats the bookies , 1998 .

[13]  Byung Ro Moon,et al.  Stock prediction based on financial correlation , 2005, GECCO '05.

[14]  Yi-Fan Wang,et al.  Predicting stock price using fuzzy grey prediction system , 2002, Expert Syst. Appl..

[15]  Tak-Chung Fu,et al.  Stock time series pattern matching: Template-based vs. rule-based approaches , 2007, Eng. Appl. Artif. Intell..

[16]  Eamonn J. Keogh,et al.  Probabilistic discovery of time series motifs , 2003, KDD '03.

[17]  H. White,et al.  Economic prediction using neural networks: the case of IBM daily stock returns , 1988, IEEE 1988 International Conference on Neural Networks.

[18]  Eamonn J. Keogh,et al.  Detecting time series motifs under uniform scaling , 2007, KDD '07.

[19]  Hui Ding,et al.  Querying and mining of time series data: experimental comparison of representations and distance measures , 2008, Proc. VLDB Endow..

[20]  Peter Tiño,et al.  Financial volatility trading using recurrent neural networks , 2001, IEEE Trans. Neural Networks.

[21]  Zehong Yang,et al.  Short-term stock price prediction based on echo state networks , 2009, Expert Syst. Appl..

[22]  Lihong Huang,et al.  Projective ART with buffers for the high dimensional space clustering and an application to discover stock associations , 2009, Neurocomputing.

[23]  Dong Ha Lee,et al.  Data mining approach to policy analysis in a health insurance domain , 2001, Int. J. Medical Informatics.

[24]  Kuniaki Uehara,et al.  Discovery of Time-Series Motif from Multi-Dimensional Data Based on MDL Principle , 2005, Machine Learning.