In this talk, we address the Cold Start problem in Demand Forecasting, focusing on scenarios where historical data is scarce or nonexistent. This constitutes a common situation in practice, such as with the launch of new products in Retail. However, many Time Series and Machine Learning models encounter difficulties in handling this challenge, primarily due to their dependence on a substantial amount of historical data for effective training and prediction.
We begin by providing an overview of established techniques used to address the Cold Start problem, including methods like padding, feature engineering, and leveraging item similarities. Additionally, we explore more recent advancements and emerging research, such as Transfer Learning for Time Series.
While each technique presents its unique set of trade-offs, the challenge lies in determining the most suitable approach for a given dataset or use case. This aspect is often not widely understood, and our goal is to unravel this complexity by offering practical insights. Furthermore, we introduce a practical framework for systematically evaluating different forecasting strategies within the Cold Start setting, guiding you in selecting the most suitable approach for your datasets and use cases.
Affiliation: paretos GmbH
I’m an experienced Data Scientist with a strong background in Software Engineering and a PhD in Mathematical Statistics. I’m interested in Machine Learning, ML Engineering and Time Series Analysis.