Deep Learning Methods for Demand Time Series Forecasting

Abstract

In the rapidly evolving landscape of demand forecasting, the challenges posed by time series forecasting in dynamic environments necessitate the exploration of advanced methodologies. This thesis seeks to bridge the gap between traditional forecasting methods and the burgeoning potential of deep learning. Drawing inspiration from work conducted at Zalando SE, a market-leading fashion retailer in Europe, the research delves into the intricacies of forecasting within the online fashion industry, where pricing and discounting strategies are pivotal in maximizing stock lifetime value.

The research underscores the significance of developing a global forecasting model, trained across a diverse assortment of articles, capable of providing article-specific predictions. Through a comprehensive exploration of feature engineering, cyclical feature transformations, and domain-specific features, the study aims to enhance the predictive capabilities of the model. Furthermore, it undertakes a comparative analysis of traditional methods such as ARIMA and ETS against novel deep learning approaches, highlighting the latter’s proficiency in capturing complex patterns across products and seasons. By navigating the challenges of forecasting, this thesis paves the way for the implementation of optimized pricing strategies and the augmentation of item profitability.