Create and deploy an interactive Energy Time Series Forecaster
Data scientist, Elia Group
Christian is a data scientist at Elia Group supporting the business in realizing their data-driven ideas, mostly focusing on time series predictions. In addition, he enjoys bridging the gap between AI & Citizen Development and providing trainings to business users.
In this workshop, we will show you how to build your own auto-Time Series forecaster with an interactive front-end dashboard. The focus will be on energy data, using open source data from Elia Group’s open data initiative.
By the end of the workshop, you will have developed your own auto-ml interactive time series forecaster. You can check it out here: https://share.streamlit.io/chris-elia/ts-forecast/main/main.py
In particular, we will cover:
How time series problems differ from other ML problems
Open Source packages for training time series models
Building an interactive front-end app in Streamlit where users can upload a new time series and get the best prediction model
Deploying your app to the cloud
Intro on how time series forecasting differentiates from other ML tasks
Getting familiar with Elia OpenData API
Integration of a time series algorithm (using Prophet)
Development of the User Interface with Streamlit
Deployment on Streamlit Cloud
Basic knowledge of Python, querying APIs, and Git
Familiar with pandas/data manipulation
IDE (like Visual Code Studio / PyCharm / etc.)
Git + GitHub account
For the workshop, we are going to use the libraries prophet and streamlit. Please create a virtual environment with the two packages installed before the workshop.