(Serious) Time for Time Series
Marysia Winkels, James Hayward
From inventory to website visitors, resource planning to financial data, time-series data is all around us. Knowing what comes next is key to success in this dynamically changing world. And for that we need reliable forecasting models. While complex & deep models may be good at forecasting, they typically give us little insight about the underlying patterns in our data. Such insights however may be a key to not only forecasting the future but shaping it.
In this tutorial, we'll cover relatively simple approaches for time series analysis and seasonality modelling with Pandas.
If you are new to time series analysis, this will be a perfect opportunity for you to get a practical tour into building simple yet powerful models. If you are a more experienced ‘fortune-teller’, you can learn how to gain interpretability and sustainability for your models without losing predictive power and with keeping hidden threats in check.
A basic understanding and/or experience with Python, pandas and scikit-learn is required.
Marysia is currently a data scientist and data science educator at GoDataDriven. She holds a BSc and MSc in Artificial Intelligence from the University of Amsterdam, and has vast industry experience in applying deep learning technologies in the medical image field.
Marysia is also co-chair of PyData Amsterdam and PyData Global, where she organises regular Python and data science meetups in and around Amsterdam as well as a yearly conference.
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