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Econometric Modeling vs Artificial Neural Networks: A Sales Forecasting Comparison
University of Borås, School of Business and IT.
2011 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesis
Abstract [en]

Econometric and predictive modeling techniques are two popular forecasting techniques. Both of these techniques have their own advantages and disadvantages. In this thesis some econometric models are considered and compared to predictive models using sales data for five products from ICA a Swedish retail wholesaler. The econometric models considered are regression model, exponential smoothing, and ARIMA model. The predictive models considered are artificial neural network (ANN) and ensemble of neural networks. Evaluation metrics used for the comparison are: MAPE, WMAPE, MAE, RMSE, and linear correlation. The result of this thesis shows that artificial neural network is more accurate in forecasting sales of product. But it does not differ too much from linear regression in terms of accuracy. Therefore the linear regression model which has the advantage of being comprehensible can be used as an alternative to artificial neural network. The results also show that the use of several metrics contribute in evaluating models for forecasting sales.

Place, publisher, year, edition, pages
University of Borås/School of Business and Informatics , 2011.
Series
Magisteruppsats ; 2010MI17
Keywords [en]
econometrics, forecasting, ARIMA, exponential smoothing, regression, neural network, ensemble, WMAPE, MAPE, MAE, RMSE, linear correlation
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hb:diva-20400Local ID: 2320/7986OAI: oai:DiVA.org:hb-20400DiVA, id: diva2:1312334
Note
Program: Magisterutbildning i informatikAvailable from: 2019-04-30 Created: 2019-04-30

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf