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https://github.com/mcallara/probabilistic-forecasting-for-planning-101
https://github.com/mcallara/probabilistic-forecasting-for-planning-101
Last synced: 7 days ago
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- Host: GitHub
- URL: https://github.com/mcallara/probabilistic-forecasting-for-planning-101
- Owner: mcallara
- Created: 2024-03-20T13:32:20.000Z (8 months ago)
- Default Branch: main
- Last Pushed: 2024-03-28T21:08:56.000Z (8 months ago)
- Last Synced: 2024-10-30T07:34:52.219Z (13 days ago)
- Language: Jupyter Notebook
- Size: 12.7 MB
- Stars: 8
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Probabilistic forecasting for planning 101
**Applied Machine Learning Days 2024**
Lausanne, Switzerland
Sunday, March 24, 2024
from 14h00 to 15h30 and from 16h00 t0 17h30 CET
Welcome! This is the companion repository of the Probabilistic forecasting for planning 101 workshop.
Join us to explore the fundamental concepts of combining probabilistic forecasting and optimization techniques to make more informed planning decisions. This is a relaxed-paced workshop that only requires basic Python programming experience and focuses on the exploration of key ideas for building and evaluating a planning system. These concepts are applicable in various domains such as retail, energy demand planning, or capacity planning.
Prerequisites: basic Python programming experience and a basic understanding of statistics are required.
## Link to this repo
[https://tinyurl.com/4dezs2m9](https://tinyurl.com/4dezs2m9)
## Authors
- Stephanie Brueckner
- Matias Callara
- Patric Hammler
- Nicolas Riesterer
## Workshop content details
Join us to explore the fundamental concepts of combining probabilistic forecasting and optimization techniques to make more informed planning decisions. This is a relaxed-paced workshop that only requires basic Python programming experience and focuses on the exploration of key ideas for building and evaluating a planning system. These concepts are applicable in various domains such as retail, energy demand planning, or capacity planning.
This workshop serves as the ideal companion for gaining a solid understanding of the core ideas presented in the Forecasting & Decision Intelligence track.
## Learning objective for participants
- Understand the Conceptual Framework
- Identify Forecasting Problems
- Differentiate between Point and Probabilistic Forecasting
- Explore Probabilistic Forecasting Models
- Familiarize yourself with popular tools
- Make Planning Decisions with Probabilistic Forecasts
- Understand Intrinsic and Extrinsic Performance Evaluation
- Explore Baselines for Comparison
- Learn about Sequential Decisions in Planning
- Recognize Limitations
## Target audience
This session is designed for individuals interested in acquiring fundamental knowledge to build, assess, and enhance planning systems by harnessing modern forecasting techniques.
Basic Python programming experience and a basic understanding of statistics are required.
## Content
[Part I - Business Forecasting with Point and Probabilistic Forecasts](https://colab.research.google.com/drive/1ecc7JH6gixHyVP1ND1pC3jUHs-yMO5tF#scrollTo=Yw7NXfmEByas)
[Part II - Practical Application of Probabilistic Forecasting](https://colab.research.google.com/github/mcallara/probabilistic-forecasting-for-planning-101/blob/main/Part_II_Practical_Application_%5BStephanie_Brueckner%5D_%5BParticipants_Version%5D.ipynb)
[Part III - Evaluating Probabilistic Forecasts](https://colab.research.google.com/github/mcallara/probabilistic-forecasting-for-planning-101/blob/main/Part_III_EvaluatingProbabilisticForecasts%5BNicolasRiesterer%5D.ipynb)
[Part IV - Planning Decisions](https://colab.research.google.com/github/mcallara/probabilistic-forecasting-for-planning-101/blob/main/Part_IV_Planning_Decisions%5BPatric_Hammler%5D.ipynb)