Feature Engineering for Time Series Forecasting - Kishan Manani
Ойын-сауық
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: github.com/KishManani/DataTal...
- Forecasting: Principles and Practice: otexts.com/fpp2/
- International Journal of Forecasting: reader.elsevier.com/reader/sd...
- Temporal Fusion Transformers for interpretable multi-horizon time series forecasting: www.sciencedirect.com/science...
- Interpretable Deep Learning for Time Series Forecasting (blog post): ai.googleblog.com/2021/12/int...
🎙 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 KZread, 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 - datatalks.club/podcast.html
📚Check our free online courses
ML Engineering course - mlzoomcamp.com
Data Engineering course - github.com/DataTalksClub/data...
MLOps course - github.com/DataTalksClub/mlop...
Analytics in Stock Markets - github.com/DataTalksClub/stoc...
LLM course - github.com/DataTalksClub/llm-...
Read about all our courses in one place - datatalks.club/blog/guide-to-...
👋🏼 GET IN TOUCH
If you want to support our community, use this link - github.com/sponsors/alexeygri...
If you’re a company, support us at alexey@datatalks.club
Пікірлер: 19
It was a great talk about data. Thank you so much. I hope you can share similar talks on the future as well
Magnificent work Bravo!!!
Great talk!
Thanks for this informative video! 👏👏👏
good to know :D I am working on RUL estimating and prognosis using time series data.
Great presentation. To clarify, is overfitting always an issue? I'm assuming it always is. In the scenario where you compute the window values, ensuring you're only using the available data... there will be no leakage at a row-level. But when you consider all training values.. for example at Time = 1 vs Time = 8, the relationships being built by the Forecasting algorithm when predicting Time = 1 will still use Time = 8 values.
For two different time series, does it make sense to build two separate models instead of having the targets of both the series in the single model (as shown at 24:40)?
In target encoding section, when product ID is encoded dynamically, how will the model distinguish between the data points belonging to same time series or different time series?
It is great but something is wrong with time_col in definition of the procedure. It seems to work if that column is an index and not mentioned in a function call.
can you explain something about stock prediction?
Hi, does anyone know how to implement the recursive forecasting that he did in Darts using sktime. I couldn't really find an intuitive explanation online.
can someone help me to deal with categorical features for forecasting time series in ML
What tools are folks using to expose/extract/generate features? Tsfresh? getML? I work in Java for my ML tasks but will happily integreate Python or C/C++ based tools into the pipeline. I'm not a statistics guy so I can't write these feature generation algos myself.
What is the name/link of the “chunky” review paper you mentioned at the end of the presentation?
@kishanmanani1466
Жыл бұрын
The paper was indeed in the references slide. It is: Petropoulos, Fotios, Daniele Apiletti, Vassilios Assimakopoulos, Mohamed Zied Babai, Devon K. Barrow, Souhaib Ben Taieb, Christoph Bergmeir et al. "Forecasting: theory and practice." International Journal of Forecasting (2022). It's also free to access online.
@RDarrylR
Жыл бұрын
@@kishanmanani1466 Thanks! I must have been looking in the wrong place!
@oneforallah
Жыл бұрын
@@kishanmanani1466 Thanks !
@AhmedThahir2002
8 ай бұрын
Hi @@kishanmanani1466 , it was a lovely talk. I was wondering if you could point me in the direction of how to implement the recursive forecasting that you in Darts using sktime. I couldn't really find an intuitive explanation online.
Great talk!