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The Effect of Optimization of Error Metrics
University of Borås, School of Business and IT.
2011 (English)Independent thesis Advanced level (degree of Master (One Year))Student thesis
Abstract [en]

It is important for a retail company to forecast its sale in correct and accurate way to be able to plan and evaluate sales and commercial strategies. Various forecasting techniques are available for this purpose. Two popular modelling techniques are Predictive Modelling and Econometric Modelling. The models created by these techniques are used to minimize the difference between the real and the predicted values. There are several different error metrics that can be used to measure and describe the difference. Each metric focuses on different properties in the forecasts and it is hence important which metrics that is used when a model is created. Most traditional techniques use the sum of squared error which have good mathematical properties but is not always optimal for forecasting purposes. This thesis focuses on optimization of three widely used error metrics MAPE, WMAPE and RMSE. Especially the metrics protection against overfitting, which occurs when a predictive model catches noise and irregularities in the data, that is not part of the sought relationship, is evaluated in this thesis. Genetic Programming, a general optimization technique based on Darwin’s theories of evolution. In this study genetic programming is used to optimize predictive models based on each metrics. The sales data of five products of ICA (a Swedish retail company) has been used to observe the effects of the optimized error metrics when creating predictive models. This study shows that all three metrics are quite poorly protected against overfitting even if WMAPE and MAPE are slightly better protected than MAPE. However WMAPE is the most promising metric to use for optimization of predictive models. When evaluated against all three metrics, models optimized based on WMAPE have the best overall result. The results of training and test data shows that the results hold in spite of overfitted models.

Place, publisher, year, edition, pages
University of Borås/School of Business and Informatics , 2011.
Series
Magisteruppsats ; 2010MI21
Keywords [en]
forecasting, regression, optimization, overfitting, MAPE, WMAPE, RMSE
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:hb:diva-20471Local ID: 2320/8187OAI: oai:DiVA.org:hb-20471DiVA, id: diva2:1312405
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
  • harvard1
  • 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