Kajanan Sangaralingam and Anindya Datta present:
Feature Engineering for Time Series Forecasting
Of all the choices made by data scientists in the course of building and operating models, feature engineering & selection is one of the most critical. Features have a substantive impact on a model’s quality, including its predictive accuracy and resilience. Unfortunately, as most ML scientists and practitioners are aware, feature engineering is more art than science. It is ad-hoc, messy, error-prone and ends up consuming 70-80% of the time and effort when building models, often resulting in sub-optimal feature selection leading to low-quality models. In this tutorial, we will introduce new ways of performing feature engineering, turning it into a systematic, procedural and scalable process, which is substantively more efficient than how it occurs currently. Participants will perform a hands-on, end-to-end, feature building exercise, with particular emphasis on feature engineering using Anovos (https://anovos.ai/ or https://github.com/anovos/anovos)
Github/Slides: https://github.com/KishManani/PyDataLondon2022
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