| language: en | |
| library_name: pytorch | |
| model_name: DataSynthis_ML_JobTask | |
| tags: | |
| - time-series | |
| - forecasting | |
| - lstm | |
| - tesla | |
| task: time-series-forecasting | |
| license: mit | |
| # DataSynthis_ML_JobTask — LSTM (Multivariate) | |
| This repo hosts a PyTorch LSTM with attention for multivariate stock forecasting (OHLCV + technicals). | |
| Included artifacts: | |
| - pytorch_model.bin | |
| - config.json | |
| - training_stats.json | |
| - preprocessing.json | |
| Quick usage: | |
| 1) Recreate the LSTM architecture from the notebook and load `pytorch_model.bin` into it | |
| 2) Use `config.json` for feature list and `seq_length` | |
| 3) Apply scaling with the parameters in `preprocessing.json`: | |
| - Transform: X_scaled = X * scale_ + min_ | |
| - Inverse: X = (X_scaled - min_) / scale_ | |