A simple LSTM model, to predict menstrual cycles and length of menstruation. Powered by Keras.
- Python3
- Keras
I use the log file of the app Period Tracker. For privacy reasons, I am not going to share my personal data, but I am happy to make a read function for the log file of a different app, if you provide me the format. The format of this log file is:
D1 M1, 20YY Period Starts D2 M1, 20YY Period Ends D3 M2, 20YY Period Starts D4 M2, 20YY Period Ends
There is also a file with synthetic data, which is used ofr training.
| Train set | Train set size | Test set size | Epochs | Acc. menstr. day | Acc. menst. length |
|---|---|---|---|---|---|
| Real data (no augmentation) | 78 | 20 | 4000 | 0.25 | 0.45 |
| Real data (x5) | 392 | 98 | 4000 | 0.98 | 0.96 |
| Real data (x5) shuffled | 392 | 98 | 4000 | 0.2 | 0.46 |
| Synthetic data | 1988 | 98 | 4000 | 0.14 | 0.38 |
| Synthetic data + Real data | 2066 | 98 | 4000 | 0.67 | 0.86 |
| Synthetic data + Real data (x5) | 2380 | 98 | 4000 | 0.98 | 0.96 |
Brownlee, Jason. “Multi-Step Time Series Forecasting with Long Short-Term Memory Networks in Python,” August 5, 2019. https://machinelearningmastery.com/multi-step-time-series-forecasting-long-short-term-memory-networks-python/.
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