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Feature Engineering for Time Series Forecasting

Feature Engineering for Time Series Forecasting

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Free

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Tips, tricks, and pitfalls to create features from time-series data to build machine learning models for forecasting - Kishan Manani

About this event

In this talk we’ll discuss all the tips, tricks, and pitfalls in creating features from time series data to build machine learning models for forecasting.

We will cover:

  • How to use traditional machine learning for forecasting, we need to convert time series data into tabular data containing useful features and a target variable.
  • The issues that arise when creating features for forecasting (e.g., look-ahead bias, distributional differences over time, one step vs multistep forecasting).
  • Feature engineering techniques to build good features for forecasting.

About the speaker:

Kishan is a machine learning and data science lead, online course instructor, and open source software contributor. He leads data science teams to deliver data and machine learning products end-to-end. He has 10+ years of experience in applying machine learning and statistics in finance, e-commerce, and healthcare research. He contributes to well known Python packages including Statsmodels, Feature-engine, and Prophet. Kishan Attained a PhD in Physics from Imperial College London in applied large scale time-series analysis and modelling of cardiac arrhythmias.

LinkedIn: https://www.linkedin.com/in/kishanmanani/

Twitter: https://twitter.com/KishManani

Feature Engineering for Time Series Forcasting course: https://www.courses.trainindata.com/p/feature-engineering-for-forecasting

Github: https://github.com/trainindata/feature-engineering-for-time-series-forecasting

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