Time Series Forecasting
Keras
English
tensorflow
time-series
menstrual-cycle-prediction
healthcare
Eval Results (legacy)
Instructions to use VishSinh/cycle-sync with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use VishSinh/cycle-sync with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://VishSinh/cycle-sync") - Notebooks
- Google Colab
- Kaggle
| { | |
| "language": "en", | |
| "tags": ["keras", "tensorflow", "time-series", "menstrual-cycle-prediction", "healthcare"], | |
| "license": "apache-2.0", | |
| "pipeline_tag": "time-series-forecasting", | |
| "model-index": [ | |
| { | |
| "name": "lstm_combined_model", | |
| "results": [ | |
| { | |
| "task": { | |
| "type": "time-series-forecasting", | |
| "name": "Menstrual Cycle Prediction" | |
| }, | |
| "metrics": [ | |
| { | |
| "type": "mae", | |
| "value": 1.2, | |
| "name": "Mean Absolute Error (MAE)" | |
| }, | |
| { | |
| "type": "mse", | |
| "value": 2.5, | |
| "name": "Mean Squared Error (MSE)" | |
| } | |
| ] | |
| } | |
| ] | |
| } | |
| ], | |
| "model-details": { | |
| "architecture": "LSTM", | |
| "input_shape": [3, 2], | |
| "output_shape": [2], | |
| "framework": "Keras", | |
| "backend": "tensorflow", | |
| "description": "An LSTM model trained on menstrual cycle data to predict next cycle length and period duration.", | |
| "trained_dataset": [ | |
| "irregular_cycle_data.csv", | |
| "regular_cycle_data.csv", | |
| "synthetic_data.csv" | |
| ], | |
| "created_by": "VishSinh", | |
| "last_trained_on": "2025-03-21" | |
| } | |
| } | |