Financial Time Series Processing: A Roadmap of Online and Offline Methods

Because financial information is a vital asset for financial and economic organizations, it requires careful management so that those organizations can enhance and facilitate the decision making process. The financial information is usually gathered over time providing a temporal and historical trace of the financial evolution in the form of time series. The organizations can then rely on such histories to understand, uncover, learn and most importantly make appropriate decisions. The present chapter tries to overview the analysis steps of financial time series and the approaches applied therein. Particular focus is given to the classification of such approaches in terms of the processing mode (i.e., online vs. offline).

[1]  Ruey S. Tsay,et al.  Analysis of Financial Time Series , 2005 .

[2]  Anna Wilbik,et al.  Analysis of Time Series via their Linguistic Summarization: the Use of the Sugeno Integral , 2007, Seventh International Conference on Intelligent Systems Design and Applications (ISDA 2007).

[3]  Robert K. Goodrich,et al.  An Algorithm for Classification and Outlier Detection of Time-Series Data , 2010 .

[4]  Won Suk Lee,et al.  estWin: adaptively monitoring the recent change of frequent itemsets over online data streams , 2003, CIKM '03.

[5]  Anna Wilbik,et al.  Linguistic summarization of time series using a fuzzy quantifier driven aggregation , 2008, Fuzzy Sets Syst..

[6]  Sameer Singh,et al.  A pattern matching tool for time-series forecasting , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[7]  Anna Wilbik,et al.  Using Fuzzy Linguistic Summaries for the Comparison of Time Series: an application to the analysis of investment fund quotations , 2009, IFSA/EUSFLAT Conf..

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

[9]  Eamonn J. Keogh,et al.  On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration , 2002, Data Mining and Knowledge Discovery.

[10]  Lotfi A. Zadeh,et al.  Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic , 1997, Fuzzy Sets Syst..

[11]  Zhong-Zhi Shi,et al.  Efficiently Mining Association Rules from Time Series , 2006 .

[12]  Jae Won Lee,et al.  Stock price prediction using reinforcement learning , 2001, ISIE 2001. 2001 IEEE International Symposium on Industrial Electronics Proceedings (Cat. No.01TH8570).

[13]  Ding-An Chiang,et al.  Mining time series data by a fuzzy linguistic summary system , 2000, Fuzzy Sets Syst..

[14]  Eamonn J. Keogh,et al.  An online algorithm for segmenting time series , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[15]  Johan Bollen,et al.  Twitter mood predicts the stock market , 2010, J. Comput. Sci..

[16]  Ching-Hsue Cheng,et al.  Fuzzy time-series based on Fibonacci sequence for stock price forecasting , 2007 .

[17]  Wei-Ngan Chin,et al.  Charting patterns on price history , 2001, ICFP '01.

[18]  Hsinchun Chen,et al.  A Discrete Stock Price Prediction Engine Based on Financial News , 2010, Computer.

[19]  Pei-Chann Chang,et al.  A TSK type fuzzy rule based system for stock price prediction , 2008, Expert Syst. Appl..

[20]  Wai Lam,et al.  Stock prediction: Integrating text mining approach using real-time news , 2003, 2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings..

[21]  Ruey S. Tsay,et al.  Analysis of Financial Time Series: Tsay/Analysis of Financial Time Series , 2005 .

[22]  Alan Liu,et al.  Pattern discovery of fuzzy time series for financial prediction , 2006, IEEE Transactions on Knowledge and Data Engineering.

[23]  Nitin Kumar,et al.  Time-series Bitmaps: a Practical Visualization Tool for Working with Large Time Series Databases , 2005, SDM.

[24]  William Leigh,et al.  A computational implementation of stock charting: abrupt volume increase as signal for movement in New York Stock Exchange Composite Index , 2004, Decis. Support Syst..

[25]  Nan Jiang,et al.  Research issues in data stream association rule mining , 2006, SGMD.

[26]  Eamonn J. Keogh,et al.  Locally Constrained Support Vector Clustering , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[27]  Jiebo Luo,et al.  The wisdom of social multimedia: using flickr for prediction and forecast , 2010, ACM Multimedia.

[28]  Anna Wilbik,et al.  Linguistic Summaries of Time Series Using a Degree of Appropriateness as a Measure of Interestingness , 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

[29]  Ravi Sankar,et al.  Time Series Prediction Using Support Vector Machines: A Survey , 2009, IEEE Computational Intelligence Magazine.

[30]  Nikola K. Kasabov,et al.  DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction , 2002, IEEE Trans. Fuzzy Syst..

[31]  Thomas N. Bulkowski.  Encyclopedia of Chart Patterns , 2000 .

[32]  Carlos Anaya,et al.  Automated Linear Modeling of Time Series with Self Adaptive Genetic Algorithms , 2007, 2007 International Joint Conference on Neural Networks.

[33]  Eamonn J. Keogh,et al.  Locally adaptive dimensionality reduction for indexing large time series databases , 2001, SIGMOD '01.

[34]  P. McNelis.  Neural networks in finance : gaining predictive edge in the market , 2005 .

[35]  T. Warren Liao,et al.  Clustering of time series data - a survey , 2005, Pattern Recognit..

[36]  Laurent Wendling,et al.  Fast polygonal approximation of digital curves , 2004, ICPR 2004.

[37]  Eamonn J. Keogh,et al.  Online discovery and maintenance of time series motifs , 2010, KDD.

[38]  Eamonn J. Keogh,et al.  Real-Time Classification of Streaming Sensor Data , 2008, 2008 20th IEEE International Conference on Tools with Artificial Intelligence.

[39]  Abdalla Kablan,et al.  Adaptive Neuro Fuzzy Inference Systems for High Frequency Financial Trading and Forecasting , 2009, 2009 Third International Conference on Advanced Engineering Computing and Applications in Sciences.

[40]  Xiang Li,et al.  A Stock Pattern Recognition Algorithm Based on Neural Networks , 2007, Third International Conference on Natural Computation (ICNC 2007).

[41]  Gareth J. Janacek,et al.  A Bit Level Representation for Time Series Data Mining with Shape Based Similarity , 2006, Data Mining and Knowledge Discovery.

[42]  Azadeh Nikfarjam,et al.  Text mining approaches for stock market prediction , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[43]  Heikki Mannila,et al.  Rule Discovery from Time Series , 1998, KDD.

[44]  Nematollaah Shiri,et al.  Fast correlation analysis on time series datasets , 2008, CIKM '08.

[45]  P. McNelis.  Neural networks in finance , 2004 .

[46]  R. Goodrich,et al.  FUZZY IMAGE PROCESSING APPLIED TO TIME SERIES ANALYSIS , 2002 .