- Friday, April 3rd
- 9:00 – 11:30 CET
What to Expect
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 into the underlying patterns in our data. Such insights, however, may be a key to not only forecasting the future but shaping it.
In this code breakfast, we will from scratch build relatively simple models that can give us such insights. We will find out why understanding seasonality is important and what data can actually tell us about it. Among other things we will learn about:
- what a time series consists of; how to decompose it and why
- naïve approaches to detecting seasonality and related dangers
- using our insights to turn simple models into powerful tools
- building blocks for seasonality from dummies to Fourier series
- fine-tuning, evaluating and interpreting models
- dealing with overfitting and other hidden challenges
- and much more!
Register for the Code Breakfast
Who Should Join
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 while keeping hidden threats in check. This code breakfast will be a combination of theory and coding, where everyone will get a chance to practice with real data.
Prerequisites & Practicalities
Basic understanding of Python, Pandas, Sklearn and time series analysis. A functional Jupyter notebook with the above libraries installed if you want to code along. Zoom application for the video call. The link to join the video conference will be distributed shortly before the date.
Vadim Nelidov is an enthusiastic data science specialist & trainer at GoDataDriven, fluent in deciphering data-driven problems. He has several years of both practical and academic experience with data science, econometrics and time series forecasting. He is passionate about sharing his knowledge and insights, believing that Data literacy should not be the privilege of a few. And his goal is to be there to make this a reality.