GitHub / AhmetZamanis / DeepLearningEnergyForecasting
Time series forecasting on an hourly energy dataset, with LSTM & Transformer models implemented in PyTorch Lightning. Deployment of the Transformer model using Docker, with GPU support.
JSON API: http://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AhmetZamanis%2FDeepLearningEnergyForecasting
PURL: pkg:github/AhmetZamanis/DeepLearningEnergyForecasting
Stars: 2
Forks: 0
Open issues: 0
License: mit
Language: Jupyter Notebook
Size: 15.2 MB
Dependencies parsed at: Pending
Created at: over 1 year ago
Updated at: 3 months ago
Pushed at: 10 months ago
Last synced at: about 1 month ago
Topics: attention-mechanism, cuda, deep-learning, docker, gaussian-processes, gpytorch, lightning, lstm, mlops, model-deployment, python, quantile-regression, recurrent-neural-networks, regression, seq2seq, time-series, time-series-forecasting, torch, transformers