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MLMU: Statistical vs. Deep Learning Methods for Time Series Forecasting

Who got it wrong?
In recent years, Deep Learning (DL) has revolutionized many fields such as image analysis, speech recognition, and natural language processing. However, Time Series Analysis is still dominated by classical statistical methods. In a recent comparison of statistical (Stat) and machine learning (ML) forecasting methods, prof. Makridakis, one of the leading authorities in this field, even claimed: „The forecasting accuracy of the best ML method was lower than the worst of Stat ones while half the ML methods were less accurate than a random walk“ (Makridakis et al., PLOS One, 2018).

In this talk, we start by a high-level introduction to time series forecasting. Next, we get an overview of M1 – M4 competitions‘ results and publicly available datasets on Kaggle. We propose an explanation why for some data, DL forecasting methods are superior, while on the other datasets, they cannot compete with Stat methods. And what can help you to choose in an era of automatically generated time series all around.

Petr Simecek recently moved to Brno and joined Central European Ai (CEAi) as a Machine Learning Engineer. Before that, he worked in the US as a Data Scientist for Google and The Jackson Laboratory. Through his career, he went from theoretical concepts (PhD. on structures of conditional independence at MFF UK) through applied statistics (genetic studies on mice at IMG AV CR & JAX) to rather practical Time Series analysis.
As a former Software Carpentry instructor, he believes in keeping doors to Data Science wide open, helping others to learn R & Python and looking for more contributors to Daily Python Tip Twitter account.

Event Organizers

Machine Learning Meetups

Date & Time

28. February 2019 18:30 - 20:30

Entry Fee

Cost for Members: Free

Cost for Non Members: Free

Updated on 15. February 2019