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.
