Feature Engineering for Time Series Forecasting - Kishan Manani

In this podcast episode, we talked with Kishan Manani about feature engineering for time series forecasting. 0:00 Introduction and Welcome 2:16 Speaker Introduction 2:54 Topic Introduction: Feature Engineering for Time Series Forecasting 4:23 Motivating Example: M5 Forecasting Competition 6:25 Machine Learning for Time Series Forecasting 8:50 Direct Forecasting vs. Recursive Forecasting 10:50 Creating Lag Features 11:45 Handling Exogenous Variables 15:55 Static Features 18:00 Time Series Cross Validation 20:00 Key Differences in Machine Learning Workflow 21:35 Feature Engineering Overview 23:00 Lag Features and Correlation Methods 29:20 Window Features 32:25 Static Features and Encoding 37:25 Avoiding Data Leakage 39:30 Useful Libraries and Tools 40:30 Example with Darts Library 45:00 Conclusions and Q&A 🔗 USEFUL LINKS - Repo and slides: https://github.com/KishManani/DataTalksClub2022 - Forecasting: Principles and Practice: https://otexts.com/fpp2/ - International Journal of Forecasting: https://reader.elsevier.com/reader/sd/pii/S0169207021001758?token=4D7E752345CC0D17393A3CDE6A7E01368B4932AC9109796C8DE6C9B7DA57BD4EB5F908C58E2C3F3A490CF7082F759DEE&originRegion=eu-west-1&originCreation=20220803074929 - Temporal Fusion Transformers for interpretable multi-horizon time series forecasting: https://www.sciencedirect.com/science/article/pii/S0169207021000637 - Interpretable Deep Learning for Time Series Forecasting (blog post): https://ai.googleblog.com/2021/12/interpretable-deep-learning-for-time.html 🎙 ABOUT THE PODCAST At DataTalksClub, we organize live podcasts that feature a diverse range of guests from the data field. Each podcast is a free-form conversation guided by a prepared set of questions, designed to learn about the guests’ career trajectories, life experiences, and practical advice. These insightful discussions draw on the expertise of data practitioners from various backgrounds. We stream the podcasts on YouTube, where each session is also recorded and published on our channel, complete with timestamps, a transcript, and important links. You can access all the podcast episodes here - https://datatalks.club/podcast.html 📚Check our free online courses ML Engineering course - http://mlzoomcamp.com Data Engineering course - https://github.com/DataTalksClub/data-engineering-zoomcamp MLOps course - https://github.com/DataTalksClub/mlops-zoomcamp Analytics in Stock Markets - https://github.com/DataTalksClub/stock-markets-analytics-zoomcamp LLM course - https://github.com/DataTalksClub/llm-zoomcamp Read about all our courses in one place - https://datatalks.club/blog/guide-to-free-online-courses-at-datatalks-club.html 👋🏼 GET IN TOUCH If you want to support our community, use this link - https://github.com/sponsors/alexeygrigorev If you’re a company, support us at [email protected]