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Attribute-based Sales Forecasting in Fashion

Show simple item record 2020-06-17T05:13:04Z 2020-06-17T05:13:04Z 2020-06-17
dc.title Attribute-based Sales Forecasting in Fashion en
ethesis.discipline Tietojenkäsittelytiede und
ethesis.department Tietojenkäsittelytieteen osasto und
ethesis.faculty Matemaattis-luonnontieteellinen tiedekunta fi
ethesis.faculty Faculty of Science en
ethesis.faculty Matematisk-naturvetenskapliga fakulteten sv
ethesis.faculty.URI Helsingin yliopisto fi University of Helsinki en Helsingfors universitet sv
dct.creator Mukhtar, Usama
dct.issued 2020
dct.language.ISO639-2 eng
dct.abstract Sales forecasting is crucial for run any retail business efficiently. Profits are maximized if popular products are available to fulfill the demand. It is also important to minimize the loss caused by unsold stock. Fashion retailers face certain challenges which make sales forecasting difficult for the products. Some of these challenges are the short life cycle of products and introduction of new products all around the year. The goal of this thesis is to study forecasting methods for fashion. We use the product attributes for products in a season to build a model that can forecast sales for all the products in the next season. Sales for different attributes are analysed for three years. Sales for different variables vary for values which indicate that a model fitted on product attributes may be used for forecasting sales. A series of experiments are conducted with multiple variants of the datasets. We implemented multiple machine learning models and compared them against each other. Empirical results are reported along with the baseline comparisons to answer research questions. Results from first experiment indicate that machine learning models are almost doing as good as the baseline model that uses mean values as predictions. The results may improve in the upcoming years when more data is available for training. The second experiment shows that models built for specific product groups are better than the generic models that are used to predict sales for all kinds of products. Since we observed a heavy tail in the data, a third experiment was conducted to use logarithmic sales for predictions, and the results do not improve much as compared to results from previous methods. The conclusion of the thesis is that machine learning methods can be used for attribute-based sales forecasting in fashion industry but more data is needed, and modeling specific groups of products bring better results. en
dct.subject sales forecasting
dct.subject machine learning
dct.subject fashion
dct.subject extreme learning machines
dct.subject support vector machines
dct.subject linear regression
dct.subject multi layer perceptron
dct.subject neural networks
dct.subject supply chain
dct.language en
ethesis.isPublicationLicenseAccepted false
ethesis.language English en
ethesis.language englanti fi
ethesis.language engelska sv
ethesis.thesistype pro gradu -tutkielmat fi
ethesis.thesistype master's thesis en
ethesis.thesistype pro gradu-avhandlingar sv
dct.identifier.ethesis E-thesisID:f8aef53b-c4b5-44bc-b49b-9c38185bf8bb
dct.identifier.urn URN:NBN:fi:hulib-202006172993
dc.type.dcmitype Text
ethesis.facultystudyline.URI none
ethesis.mastersdegreeprogram.URI none

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