Neighborhood counting for financial time series forecasting

Time series data abound and analysis of such data is challenging and potentially rewarding. One example is financial time series analysis. Most of the intelligent data analysis methods can be applied in principle, but evolutionary computing is becoming increasingly popular and powerful.

[1]  Dimitrios Gunopulos,et al.  Time series similarity measures (tutorial PM-2) , 2000, KDD '00.

[2]  Haixun Wang,et al.  Landmarks: a new model for similarity-based pattern querying in time series databases , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[3]  Christos Faloutsos,et al.  Efficient retrieval of similar time sequences under time warping , 1998, Proceedings 14th International Conference on Data Engineering.

[4]  Man Hon Wong,et al.  Fast time-series searching with scaling and shifting , 1999, PODS '99.

[5]  Dimitrios Gunopulos.  Time Series Similarity Measures , 2005 .

[6]  Dimitrios Gunopulos,et al.  Indexing multi-dimensional time-series with support for multiple distance measures , 2003, KDD '03.

[7]  Andrzej Skowron,et al.  Hyperrelations in version space , 2004, Int. J. Approx. Reason..

[8]  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.

[9]  Olivier V. Pictet,et al.  Changing time scale for short‐term forecasting in financial markets , 1996 .

[10]  Tripti Negi,et al.  Time Series : Similarity Search and its Applications , 2004 .

[11]  Gregory Piatetsky-Shapiro,et al.  Measuring real-time predictive models , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[12]  Hui Wang,et al.  Nearest neighbors by neighborhood counting , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Hui Wang,et al.  All Common Subsequences , 2007, IJCAI.

[14]  Wesley W. Chu,et al.  An index-based approach for similarity search supporting time warping in large sequence databases , 2001, Proceedings 17th International Conference on Data Engineering.

[15]  Donald J. Berndt,et al.  Using Dynamic Time Warping to Find Patterns in Time Series , 1994, KDD Workshop.

[16]  Wesley W. Chu,et al.  Efficient searches for similar subsequences of different lengths in sequence databases , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[17]  Leonardo Vanneschi,et al.  Operator-Based Distance for Genetic Programming: Subtree Crossover Distance , 2005, EuroGP.

[18]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[19]  Werner Dubitzky,et al.  A flexible and robust similarity measure based on contextual probability , 2005, IJCAI.

[20]  Mak Kaboudan,et al.  Biologically Inspired Algorithms for Financial Modelling , 2006, Genetic Programming and Evolvable Machines.

[21]  Ambuj K. Singh,et al.  Similarity searching for multi-attribute sequences , 2002, Proceedings 14th International Conference on Scientific and Statistical Database Management.

[22]  Nasser Yazdani,et al.  Sequence matching of images , 1996, Proceedings of 8th International Conference on Scientific and Statistical Data Base Management.

[23]  Daniel S. Hirschberg,et al.  Algorithms for the Longest Common Subsequence Problem , 1977, JACM.

[24]  Eamonn J. Keogh,et al.  Exact indexing of dynamic time warping , 2002, Knowledge and Information Systems.

[25]  Kyuseok Shim,et al.  Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases , 1995, VLDB.

[26]  Ronan Cummins,et al.  Evolving local and global weighting schemes in information retrieval , 2006, Information Retrieval.

[27]  Christos Faloutsos,et al.  Efficient Similarity Search In Sequence Databases , 1993, FODO.

[28]  Michael O'Neill,et al.  Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series) , 2005 .

[29]  S. S. Stevens.  Mathematics, measurement, and psychophysics. , 1951 .

[30]  S. Luke.  When short runs beat long runs , 2001 .

[31]  Dimitrios Gunopulos,et al.  Finding Similar Time Series , 1997, PKDD.

[32]  Xue Li,et al.  Time weight collaborative filtering , 2005, CIKM '05.

[33]  Hui Wang,et al.  Data Mining for Financial Decision Making , 2002, Decis. Support Syst..

[34]  Euripides G. M. Petrakis,et al.  Similarity Searching in Medical Image Databases , 1997, IEEE Trans. Knowl. Data Eng..

[35]  Fionn Murtagh,et al.  Wavelet-based combined signal filtering and prediction , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[36]  Dina Q. Goldin,et al.  On Similarity Queries for Time-Series Data: Constraint Specification and Implementation , 1995, CP.