Time Series Forecasting in Python a talk by Petr Šimeček

Friday, 14 June, 11:50 in Club

This talk should give you a high-level introduction to time series forecasting in Python. In particular, I want to focus on differences between classical statistical approaches (exponential smoothing, ARIMA…) and more recent machine learning methods (neural networks).

I will also describe the history of M1-M4 time series forecasting competitions and provide examples on public Kaggle datasets.

How is it possible that deep neural networks are superior on one type of data while for a different dataset they give forecast so poor that prof. Makridakis summarized his findings (Makridakis et al., Plos One, 2018) as follows: “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.”

In the end, I will provide some hints of which method is likely to work best for your data.

This talk is suitable for both beginner and advanced Pythonistas.
Is part of the PyData track

Petr Šimeček

While my background is Probability and Statistics (Ph.D. at MFF UK), I have never truly worked with time series data until two years ago. I still remember many WTF moments in the beginning and hope to share these with the audience.

I have recently moved from California to Brno and joined Central European Ai (CEAi) as an ML Engineer. I keep discovering Python and tweeting about it at @python_tip.

simecek simecek