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 workshop, we'll cover simple but powerful approaches for time series analysis and seasonality modelling. 

Attendees will be able to analyse time series data with Pandas, and decompose time series data to trend, seasonality and error. 

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.