AR-204 on JIRA. Learning from time series to predict future, in short and long range.
Notebooks are in the notebooks/ folder.
- sandbox.ipynb --- ignore this.
- monitor-data.ipynb --- extracting raw monitor data from the dataset.
- visualize-predicts.ipynb --- visualization of predictions for a single stay.
- predict-stats.ipynb --- prediction performance evaluation.
Run <command> -h to get help on command-line options.
- extract --- extracts contiguous fragments from data for training
- prepare --- prepares the dataset as a tensor for training
- train --- trains a model on the dataset
- predict --- extends a stay with predictions and log-likelihoods of observations.
The stays are in data/. The order of preparing a data set for training is
- extract
- prepare
Use train to train a model. The depth must be greater than 1 for the model
to learn to predict more than a single step in the future and deal with
missing values.
Use predict to extend a frame with prediction. Models are in models/, data
folder should be a subdirectiory of data/ (with the list of concept names as
the name) and the stay is a pickle file in data/. The model and the data
must match. Example:
predict model/ALL-15.model data/ALL data/monitor-dataset-Ichilov_MICU_20194.pkl
- scripts/extract.sh --- extracts contiguous fragments (120 min by default).
- scripts/train.sh --- trains a model.
For scripts, modify defaults by specifying environment variables on the command line. For example:
RATE=0.0001 scripts/train.sh
to set the training rate to 0.0001. Consult the scripts for available variables.