Speaker:: Marysia Winkels James Hayward
Track: PyData: PyData & Scientific Libraries Stack 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.
Recorded at the PyConDE & PyData Berlin 2022 conference, April 11-13 2022. https://2022.pycon.de More details at the conference page: https://2022.pycon.de/program/DTTQ9D Twitter: https://twitter.com/pydataberlin Twitter: https://twitter.com/pyconde