Change search
CiteExportLink to record
Permanent link

Direct link
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
Defining and predicting fast-selling clothing options
Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
2019 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This thesis aims to find a definition of fast-selling clothing options and to find a way to predict them using only a few weeks of sale data as input. The data used for this project contain daily sales and intake quantity for seasonal options, with sale start 2016-2018, provided by the department store chain Åhléns.

A definition is found to describe fast-selling clothing options as those having sold a certain percentage of their intake after a fixed number of days. An alternative definition based on cluster affiliation is proven less effective.

Two predictive models are tested, the first one being a probabilistic classifier and the second one being a k-nearest neighbor classifier, using the Euclidean distance. The probabilistic model is divided into three steps: transformation, clustering, and classification. The time series are transformed with B-splines to reduce dimensionality, where each time series is represented by a vector with its length and B-spline coefficients. As a tool to improve the quality of the predictions, the B-spline vectors are clustered with a Gaussian mixture model where every cluster is assigned one of the two labels fast-selling or ordinary, thus dividing the clusters into disjoint sets: one containing fast-selling clusters and the other containing ordinary clusters. Lastly, the time series to be predicted are assumed to be Laplace distributed around a B-spline and using the probability distributions provided by the clustering, the posterior probability for each class is used to classify the new observations.

In the transformation step, the number of knots for the B-splines are evaluated with cross-validation and the Gaussian mixture models, from the clustering step, are evaluated with the Bayesian information criterion, BIC. The predictive performance of both classifiers is evaluated with accuracy, precision, and recall. The probabilistic model outperforms the k-nearest neighbor model with considerably higher values of accuracy, precision, and recall. The performance of each model is improved by using more data to make the predictions, most prominently with the probabilistic model.

Place, publisher, year, edition, pages
2019. , p. 44
Keywords [en]
Apparel retail, B-splines, Gaussian mixture model, Probabilistic prediction, Sales prediction
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-158190ISRN: LIU-IDA/STAT-A--19/011--SEOAI: oai:DiVA.org:liu-158190DiVA, id: diva2:1331012
External cooperation
Åhléns
Subject / course
Statistics
Supervisors
Examiners
Available from: 2019-06-27 Created: 2019-06-26 Last updated: 2019-06-27Bibliographically approved

Open Access in DiVA

fulltext(8229 kB)11 downloads
File information
File name FULLTEXT01.pdfFile size 8229 kBChecksum SHA-512
0798c833783512abe778bb531e7ce16913ac01001ca777b7f372709f5407dc4adf16c4af7d98f3c6f16529951e0d812947a3f7c97ba3cf215c579851fc0c7d5e
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Jesperson, Sara
By organisation
The Division of Statistics and Machine Learning
Probability Theory and Statistics

Search outside of DiVA

GoogleGoogle Scholar
Total: 11 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 68 hits
CiteExportLink to record
Permanent link

Direct link
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