Time Series Forecasting
Keras
English
time-series
stock-forecasting
LSTM
ARIMA
Prophet
machine-learning
deep-learning
forecasting
Instructions to use Hiruni2207/DataSynthis_ML_JobTask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use Hiruni2207/DataSynthis_ML_JobTask with Keras:
# !pip install -U keras tensorflow huggingface_hub # Keras needs TensorFlow installed to read "hf://" paths, so the tensorflow backend is selected here; # "jax" and "torch" also work for computation once TensorFlow is installed. import os os.environ["KERAS_BACKEND"] = "tensorflow" import keras model = keras.saving.load_model("hf://Hiruni2207/DataSynthis_ML_JobTask") - Notebooks
- Google Colab
- Kaggle
Download performance_summary.csv from Hiruni2207/DataSynthis_ML_JobTask: direct link, hf CLI and curl.
- Browser
- Download file 147 Bytes
-
https://huggingface.co/Hiruni2207/DataSynthis_ML_JobTask/resolve/0995c199063d4795b8f313f70a80f4e25fcbbc0d/performance_summary.csv
- Command line
-
hf download hf://Hiruni2207/DataSynthis_ML_JobTask@0995c199063d4795b8f313f70a80f4e25fcbbc0d/performance_summary.csv
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curl -L -o performance_summary.csv https://huggingface.co/Hiruni2207/DataSynthis_ML_JobTask/resolve/0995c199063d4795b8f313f70a80f4e25fcbbc0d/performance_summary.csv
147 Bytes
| Model,RMSE,MAPE | |
| ARIMA,3.3747959050510263,1.89735011501022 | |
| Prophet,4.7650432414652215,3.1859116338570725 | |
| LSTM,2.0890386404819212,1.2515893580288606 | |