PyData Rhein-Main: Boosting Time Series Accuracy by Robert Haase (Paretos)

This talk explores the practical application of ensemble methods in time series analysis, based on Robert’s extensive experience at Pareto. It covers various ensembling approaches, highlighting their effectiveness in different real-world scenarios. Attendees will gain insights into which methods perform best in practice, supported by behind-the-scenes examples of successful implementations. The session provides valuable strategies for improving predictive accuracy, making it ideal for anyone looking to leverage ensemble techniques in their time series projects.
Robert earned both his Bachelor's and Master's degrees in Physics from the University of Heidelberg, specializing in Condensed Matter Physics and Computational Physics. During his Master's thesis in 2020, he advanced existing NLP Transformer architectures for timeseries applications. This involved Robert working extensively with uncertainty quantifications and normalizing flows. Since the beginning of 2021, he has been employed at Paretos, where the primary focus of his work lies in Timeseries Forecasting, specifically demand forecasting. Robert has a keen interest in combining traditional statistical methods with deep learning techniques.

Acknowledgements
Hessian.AI, for hosting the meetup.
https://hessian.ai/
PIONEERS HUB, for organising.
https://pioneershub.org/

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