A package to train and predict the end of a process from history logs
Project description
Predicting Remaining Cycle Time from Ongoing Case
Predicting the remaining cycle time of ongoing cases is one important use case of predictive process monitoring.
It is machine learning approach based on survival analysis that can learn from complete/ongoing traces.
we train a neural network to predict the probability density function of the remaining cycle time of a running case.
Documentation:
https://fazaki.github.io/cycle_prediction/
Getting started:
A) pip installation
1. Cd to home dir
cd ~
2. Initialize a virtualenv that uses the Python 3.7 available at home directory
virtualenv -p ~/python-3.7/bin/python3 PROJECTNAME
3. Activate the virtualenv
Windows:
source ~/PROJECTNAME/Scripts/activate
Linux:
source ~/PROJECTNAME/bin/activate
4. Install below packages
pip install cycle-prediction
5. Create a new kernel with the same project name
pip install -U pip ipykernel
ipython kernel install --user --name=PROJECTNAME
6. Use the example notebook
B) Source code installation:
1. Cd to home dir
cd ~
2. Initialize a virtualenv that uses the Python 3.7 available at home directory
Virtualenv -p ~/python-3.7/bin/python3 PROJECTNAME
3. Activate the virtualenv
Windows:
source ~/PROJECTNAME/Scripts/activate
Linux:
source ~/PROJECTNAME/bin/activate
4. Install ipykernel
pip install -U pip ipykernel
5. Clone the repo
git clone https://github.com/fazaki/time-to-event/tree/master
cd time-to-event
6. Install required dependencies:
pip install -e .
7. Use the example notebook
Theory
- Paper publication in progress
References
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