A hybrid sales forecasting system based on clustering and decision trees

Competition and globalization imply a very accurate production and sourcing management of the Textile-Apparel-Distribution network actors. A sales forecasting system is required to respond to the versatile textile market and the needs of the distributors. Nowadays, due to the specific constraints of the textile sales (numerous and new items, short life time), existing forecasting models are generally unsuitable or unusable. We propose a forecasting system, based on clustering and classification tools, which performs mid-term forecasting. Performances of our models are evaluated using real data from an important French textile distributor.

[1]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

[2]  Krzysztof J. Cios,et al.  A machine learning method for generation of a neural network architecture: a continuous ID3 algorithm , 1992, IEEE Trans. Neural Networks.

[3]  Manuel Landajo,et al.  Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case , 2005, Eur. J. Oper. Res..

[4]  Jae Hong Park,et al.  Composite modeling for adaptive short-term load forecasting , 1991 .

[5]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[6]  David W. Aha,et al.  Tolerating Noisy, Irrelevant and Novel Attributes in Instance-Based Learning Algorithms , 1992, Int. J. Man Mach. Stud..

[7]  Antonio Fiordaliso.  Autostructuration of fuzzy systems by rules sensitivity analysis , 2001, Fuzzy Sets Syst..

[8]  Wei Tang,et al.  Ensembling neural networks: Many could be better than all , 2002, Artif. Intell..

[9]  Lieva Van Langenhove,et al.  Automotive industry a high potential market for nonwovens sound insulation , 2004 .

[10]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[11]  József Dombi,et al.  Learning multicriteria classification models from examples: Decision rules in continuous space , 2005, Eur. J. Oper. Res..

[12]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[13]  H. L. Le Roy,et al.  Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; Vol. IV , 1969 .

[14]  Fred L. Collopy,et al.  Error Measures for Generalizing About Forecasting Methods: Empirical Comparisons , 1992 .

[15]  Fritz Wysotzki,et al.  Automatic construction of decision trees for classification , 1994, Ann. Oper. Res..

[16]  Pat Langley,et al.  Estimating Continuous Distributions in Bayesian Classifiers , 1995, UAI.

[17]  R. Gray.  Entropy and Information Theory , 1990, Springer New York.

[18]  Salvatore Greco,et al.  Rough sets theory for multicriteria decision analysis , 2001, Eur. J. Oper. Res..

[19]  Joachim Diederich,et al.  Survey and critique of techniques for extracting rules from trained artificial neural networks , 1995, Knowl. Based Syst..

[20]  Paris A. Mastorocostas,et al.  A constrained orthogonal least-squares method for generating TSK fuzzy models: Application to short-term load forecasting , 2001, Fuzzy Sets Syst..

[21]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[22]  Constantin Zopounidis,et al.  Multicriteria classification and sorting methods: A literature review , 2002, Eur. J. Oper. Res..

[23]  P. Grünwald.  The Minimum Description Length Principle (Adaptive Computation and Machine Learning) , 2007 .

[24]  Robert C. Holte,et al.  Very Simple Classification Rules Perform Well on Most Commonly Used Datasets , 1993, Machine Learning.

[25]  Manuel Landajo,et al.  Forecasting business profitability by using classification techniques: A comparative analysis based on a Spanish case , 2005, Eur. J. Oper. Res..

[26]  Michel Happiette,et al.  AN AUTOMATIC TEXTILE SALES FORECAST USING FUZZY TREATMENT OF EXPLANATORY VARIABLES , 2002 .

[27]  J. Ross Quinlan,et al.  Induction of Decision Trees , 1986, Machine Learning.

[28]  A. Fiordaliso.  A nonlinear forecasts combination method based on Takagi–Sugeno fuzzy systems , 1998 .

[29]  Bernard Hugueney.  Représentations symboliques de longues séries temporelles , 2003 .

[30]  Antonella Meneghetti,et al.  The production planning process for a network of firms in the textile-apparel industry , 2000 .

[31]  Jacek M. Zurada,et al.  Computational intelligence methods for rule-based data understanding , 2004, Proceedings of the IEEE.

[32]  M. Happiette,et al.  A Short and Mean Term Forecasting System Adapted to Textile Items' Sales , 2002 .

[33]  Esa Alhoniemi,et al.  Clustering of the self-organizing map , 2000, IEEE Trans. Neural Networks Learn. Syst..

[34]  Roshdy S. Youssif,et al.  Combining genetic algorithms and neural networks to build a signal pattern classifier , 2004, Neurocomputing.

[35]  S. C. Johnson.  Hierarchical clustering schemes , 1967, Psychometrika.

[36]  T. Hesterberg,et al.  A regression-based approach to short-term system load forecasting , 1989, Conference Papers Power Industry Computer Application Conference.

[37]  Gerhard-Wilhelm Weber,et al.  CLUSTER ALGORITHMS: THEORY AND METHODS ¤ , 2002 .

[38]  Wolfgang Müller,et al.  Applying decision tree methodology for rules extraction under cognitive constraints , 2002, Eur. J. Oper. Res..

[39]  Michel Happiette,et al.  A short and mean-term automatic forecasting system--application to textile logistics , 2005, Eur. J. Oper. Res..

[40]  J. Scott Armstrong,et al.  On the Selection of Error Measures for Comparisons Among Forecasting Methods , 2005 .

[41]  R. J. Kuo,et al.  Fuzzy neural networks with application to sales forecasting , 1999, Fuzzy Sets Syst..

[42]  Edward I. Altman,et al.  Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience) , 1994 .

[43]  J. Scott Armstrong,et al.  Principles of forecasting : a handbook for researchers and practitioners , 2001 .

[44]  John A. Hartigan,et al.  Clustering Algorithms , 1975 .

[45]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[46]  H. Yoo,et al.  Short term load forecasting using a self-supervised adaptive neural network , 1999 .

[47]  Kun Chang Lee,et al.  An intelligent approach to time series identification by a neural network-driven decision tree classifier , 1996, Decis. Support Syst..

[48]  Mark Last,et al.  A compact and accurate model for classification , 2004, IEEE Transactions on Knowledge and Data Engineering.

[49]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[50]  Sanjoy Ghose,et al.  Comparing the predictive performance of a neural network model with some traditional market response models , 1994 .

[51]  Shogo Nishida,et al.  Implementation and refinement of decision trees using neural networks for hybrid knowledge acquisition , 1995, Artif. Intell. Eng..

[52]  J. Scott Armstrong,et al.  Evaluation of Extrapolative Forecasting Methods: Results of a Survey of Academicians and Practitioners , 1982 .

[53]  Simon Kasif,et al.  A System for Induction of Oblique Decision Trees , 1994, J. Artif. Intell. Res..

[54]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[55]  S. T. Buckland,et al.  An Introduction to the Bootstrap. , 1994 .

[56]  Daniel S. Hirschberg,et al.  The Time Complexity of Decision Tree Induction , 1995 .

[57]  Zhi-Hua Zhou,et al.  NeC4.5: Neural Ensemble Based C4.5 , 2004, IEEE Trans. Knowl. Data Eng..

[58]  Usama M. Fayyad,et al.  Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning , 1993, IJCAI.

[59]  Shimon Schocken,et al.  Neural Networks for Decision Support: Problems and Opportunities , 1991, Decis. Support Syst..

[60]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[61]  Pat Langley,et al.  An Analysis of Bayesian Classifiers , 1992, AAAI.

[62]  Jean-Yves Potvin,et al.  An interactive-graphic environment for automatic generation of decision trees , 1996, Decis. Support Syst..

[63]  Patrick A. Thompson,et al.  An MSE statistic for comparing forecast accuracy across series , 1990 .