Time Series Forecasting
Transformers
Safetensors
English
time-series
forecasting
patchtst
electricity
eirgrid
energy
Instructions to use Priyansu19/patchtst-eirgrid-forecaster with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Priyansu19/patchtst-eirgrid-forecaster with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Priyansu19/patchtst-eirgrid-forecaster", dtype="auto") - Notebooks
- Google Colab
- Kaggle
PatchTST EirGrid Forecaster ⚡
This is a PatchTST model fine-tuned on EirGrid (Irish Grid) System Data to forecast electricity demand.
Model Details
- Architecture: PatchTST (Transformer-based Time Series)
- Base Model:
ibm-granite/granite-timeseries-patchtst - Task: Long-term Forecasting (96-hour horizon)
- Input: 512 hours of historical load data.
- Output: 96 hours of future load forecast.
Usage
from transformers import PatchTSTForPrediction
# Load the model
model = PatchTSTForPrediction.from_pretrained("Priyansu19/patchtst-eirgrid-forecaster")
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for Priyansu19/patchtst-eirgrid-forecaster
Base model
ibm-granite/granite-timeseries-patchtst