| .gitignore | ||
| price_actual_over_time.png | ||
| pyproject.toml | ||
| README.md | ||
| ShapTime.py | ||
| tsforecast.ipynb | ||
Time series forecasting energy prices
With features representing energy generation of various sources (oil/biomass/hydro)
Setup
Prerequisites:
- Have a working version of
pippreferably in Python 3.12
Steps
- Install
uv
pip install uv
- Create & activate virtual environment
uv venv .venv
# on Windows
.\.venv\Scripts\activate
# on Linux:
source .venv/bin/activate
- Install the dependencies
uv pip install .
Get hackin'
Look in tsforecast.ipynb
- Play around with some of the techniques
- Seasonal decompose
- SARIMA
- XGBoost
- LSTM
- Try to beat my MAE on test set of ~0.31 (I used LSTM)
