Stream Time Series Approach for Supporting Business Intelligence

Business intelligence has an important role in effective decision making to improve the business performance and opportunities by understanding the organization’s environments through the systematic process of information. This paper proposes a novel framework based on data mining technologies for making a prediction of business environment. We present a business intelligence model to predict the business performance by using dimensionality reduction as preprocessing data then applying Sequential Minimal Optimization based on the Support Vector Machine algorithm to generate future data. To examine the approach, we apply them on stock price data set obtained from Yahoo Finance.

[1]  Carlo Vercellis,et al.  Business Intelligence: Data Mining and Optimization for Decision Making , 2009 .

[2]  Mostafa Jafari,et al.  Evaluation model of business intelligence for enterprise systems using fuzzy TOPSIS , 2012, Expert Syst. Appl..

[3]  Pu Han,et al.  SMO Algorithm Applied in Time Series Model Building and Forecast , 2007, 2007 International Conference on Machine Learning and Cybernetics.

[4]  Chi-Chen Wang,et al.  A comparison study between fuzzy time series model and ARIMA model for forecasting Taiwan export , 2011, Expert Syst. Appl..

[5]  Eamonn J. Keogh,et al.  Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases , 2001, Knowledge and Information Systems.

[6]  Xiang Lian,et al.  Multiscale Representations for Fast Pattern Matching in Stream Time Series , 2009, IEEE Transactions on Knowledge and Data Engineering.

[7]  Shiliang Sun,et al.  A review of optimization methodologies in support vector machines , 2011, Neurocomputing.

[8]  Qiang Wang,et al.  A dimensionality reduction technique for efficient time series similarity analysis , 2008, Inf. Syst..

[9]  Jayanthi Ranjan.  Business justification with business intelligence , 2008 .

[10]  Tak-Chung Fu,et al.  A review on time series data mining , 2011, Eng. Appl. Artif. Intell..

[11]  David R. Anderson,et al.  Understanding AIC and BIC in Model Selection , 2004 .

[12]  Wen-ying Liu,et al.  Application of Least Squares Support Vector Machine(LS-SVM) Based on Time Series in Power System Monthly Load Forecasting , 2011, 2011 Asia-Pacific Power and Energy Engineering Conference.

[13]  Chonghui Guo,et al.  Similarity measure based on piecewise linear approximation and derivative dynamic time warping for time series mining , 2011, Expert Syst. Appl..

[14]  Tak-Chung Fu,et al.  Representing financial time series based on data point importance , 2008, Eng. Appl. Artif. Intell..

[15]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[16]  J. Platt.  Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines , 1998 .

[17]  Jirong Wang,et al.  Selecting training points of the sequential minimal optimization algorithm for Support Vector Machine , 2011, 2011 2nd International Conference on Control, Instrumentation and Automation (ICCIA).

[18]  Antti Lönnqvist,et al.  The Measurement of Business Intelligence , 2005, Inf. Syst. Manag..

[19]  Yuhong Yang,et al.  Assessing Forecast Accuracy Measures , 2004 .

[20]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[21]  Francis Eng Hock Tay,et al.  Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

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

[23]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[24]  Sanjay Kumar,et al.  INTUITIONISTIC FUZZY SETS BASED METHOD FOR FUZZY TIME SERIES FORECASTING , 2012, Cybern. Syst..

[25]  Rob J Hyndman,et al.  Automatic Time Series Forecasting: The forecast Package for R , 2008 .

[26]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[27]  Wai Keung Wong,et al.  Adaptive Time-Variant Models for Fuzzy-Time-Series Forecasting , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[28]  Mehdi Khashei,et al.  A novel hybridization of artificial neural networks and ARIMA models for time series forecasting , 2011, Appl. Soft Comput..

[29]  Eamonn J. Keogh,et al.  iSAX 2.0: Indexing and Mining One Billion Time Series , 2010, 2010 IEEE International Conference on Data Mining.

[30]  Seok-Ju Chun,et al.  Representation and clustering of time series by means of segmentation based on PIPs detection , 2010, 2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE).

[31]  T. Miranda Lakshmi,et al.  An analysis on business intelligence models to improve business performance , 2012, IEEE-International Conference On Advances In Engineering, Science And Management (ICAESM -2012).