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Intelligent sales forecasting engine using genetic algorithms

Authors:

Kamal MehtaAuthors Info & Claims

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

Pages 1669 - 1672

https://doi.org/10.1145/1871437.1871700

Published: 26 October 2010 Publication History

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Abstract

Times series techniques have been extensively used for Sales forecasting. Research has established that a combination forecast works better than a single forecast. Our research attempts to design an Intelligent Forecasting Engine which will use a combination forecasting technique. This design is based on use of Genetic Algorithms, for selecting the best methods to combine for forecasting. Early results demonstrate that Genetic Algorithms have the potential to become a powerful tool for time series sales forecasting.

References

[1]

Makridakis, S., Wheelwright, S., and Hyndman, R. (1998). Forecasting methods and Applications. Third Edition. Wiley: NY.

Google Scholar

[2]

Bates, J. M., & Granger, C. W. J. (1969). Combination of forecasts. Operations Research Quarterly, 20, 451--468.

Crossref

Google Scholar

[3]

Granger, C., & Ramanathan, R. (1984). Improved methods of combining forecasts. Journal of Forecasting, 3, 197--204.

Crossref

Google Scholar

[4]

J. Scott Armstrong. October 1990, Combining forecasts: The end of the beginning or the beginning of the end? International Journal of Forecasting, 5(4):585--588, (Reformatted September 2006)

Crossref

Google Scholar

[5]

S. Makridakis and M. Hibon. 2000, The M3-Competition: Results, Conclusions and Implications. International Journal of Forecasting, 16(4):451--476.

Crossref

Google Scholar

[6]

R. Singh, "On using various decomposition methods in time series forecasting," Master's thesis, Kanwal Rekhi School of Information Technology, IIT Bombay, Powai, Bombay, 400076, INDIA, 2006.

Google Scholar

[7]

B. Menezes, A. Seth, and R. Singh, 2007, "Can a million experts improve your sales' forecasts?" European Symposium on Time Series Prediction.

Google Scholar

[8]

Mitchell M. An Introduction to Genetic Algorithms (MIT press, Cambridge, USA. 1996.)

Digital Library

Google Scholar

[9]

Beasley, D., Bull, D.R., Martin, R. R., 1993. An overview of genetic algorithms: part I, fundamentals. University Computing 15.

Google Scholar

[10]

Sam Mahfoud and Ganesh, 1996, Mani "Financial Forecasting Using genetic Algorithms", Applied Artificial Intelligence, 10:543± 565.

Google Scholar

[11]

Economagic.com: Economic time series page. http://www.economagic.com/.

Google Scholar

[12]

Rob Hyndman. Time series data library. http://www-personal.buseco.monash. edu.au/_hyndman/TSDL/.

Google Scholar

Cited By

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Khan MSaqib SAlyas TUr Rehman ASaeed YZeb AZareei MMohamed E(2020)Effective Demand Forecasting Model Using Business Intelligence Empowered With Machine LearningIEEE Access10.1109/ACCESS.2020.30037908(116013-116023)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3003790
CATAL CECE KArslan BAkbulut A(2019)Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales ForecastingBalkan Journal of Electrical and Computer Engineering10.17694/bajece.494920(20-26)Online publication date: 31-Jan-2019
https://doi.org/10.17694/bajece.494920

Index Terms

Intelligent sales forecasting engine using genetic algorithms
1. Applied computing
  1. Operations research
    1. Decision analysis
2. Information systems
  1. Information systems applications
    1. Data mining
    2. Decision support systems

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Published In

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

October 2010

2036 pages

ISBN:9781450300995

DOI:10.1145/1871437

General Chair:
Jimmy Huang
York University, Canada
,
Program Chairs:
Nick Koudas
University of Toronto, Canada
,
Gareth Jones
Dublin City University, Ireland
,
Xindong Wu
University of Vermont, USA
,
Kevyn Collins-Thompson
Microsoft Research, USA
,
Aijun An
York University, Canada

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 26 October 2010

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October 26 - 30, 2010

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Khan MSaqib SAlyas TUr Rehman ASaeed YZeb AZareei MMohamed E(2020)Effective Demand Forecasting Model Using Business Intelligence Empowered With Machine LearningIEEE Access10.1109/ACCESS.2020.30037908(116013-116023)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3003790
CATAL CECE KArslan BAkbulut A(2019)Benchmarking of Regression Algorithms and Time Series Analysis Techniques for Sales ForecastingBalkan Journal of Electrical and Computer Engineering10.17694/bajece.494920(20-26)Online publication date: 31-Jan-2019
https://doi.org/10.17694/bajece.494920

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Recommendations

Sales forecasting for Chemical Products by Using SARIMA Model

A two-stage dynamic sales forecasting model for the fashion retail

A hybrid SARIMA wavelet transform method for sales forecasting

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