2024-04-23 –, B09
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.
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.
None
Expected audience expertise: Domain –Intermediate
Abstract as a tweet (X) or toot (Mastodon) –Exploring the Cold Start problem in Demand Forecasting. Overcoming difficulties faced by Time Series and ML models. Uncover practical techniques and a systematic evaluation framework for effective forecasting.
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.
Data Scientist from Heidelberg, Germany. The central focus of my work is time series forecasting, with a specific emphasis on forecasting demand. Before my current role, I gained experience as a Research Assistant focusing on astrophysics and data analysis.