Workshop / Overview

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. 

Workshop / Outcome

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

Workshop / Difficulty

Beginner level

Workshop / Prerequisites

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. 

Track / Co-organizers

Marysia Winkels

Data Scientist, GoDataDriven

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