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.
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