This repository provides Python code for implementing the estimation procedure from Chen et al. (2023). The tutorial demonstrates how to estimate the arrival rate of customers from a simulated dataset.
Please refer to Appendix A of Chen et al. (2023) for details of the procedure, which is a simpler variant of the one proposed in Chen, Lee, and Negahban (2019).
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Chen, Gurlek, Lee, Shen (2023): Can customer arrival rates be modelled by sine waves? (Joint issue in Service Science and Stochastic Systems, forthcoming)
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Chen, Lee, Negahban (2019): Super-resolution estimation of cyclic arrival rates (Annals of Statistics 47:3:1754-1775)
We recommend creating a virtual Python environment to ensure the correct versioning of all dependencies. To this end, run the lines below in an Anaconda Prompt
to create a virtual environment called sine-NHPP, and then activate it.
conda create -n sine-NHPP python=3.9.13
conda activate sine-NHPP
Clone this repository, or manually download the files and extract them to a directory called sine-NHPP. Then go to the directory:
cd sine-NHPP
Install the dependencies by running the following lines in the Anaconda Prompt:
pip install numpy==1.21.5
pip install pandas==2.0.2
pip install matplotlib==3.7.1
pip install jupyter
Run the tutorial sine-NHPP_tutorial.ipynb for a demonstration of the estimation procedure.
jupyter notebook sine-NHPP_tutorial.ipynb