Determining the optimal piecewise constant approximation for the Nonhomogeneous Poisson Process rate of Emergency Department patient arrivals

This project is an academic reference implementation for a specific paper (arXiv:2101.11138). With 0 stars and 5 forks over 5 years, it lacks any community traction or software ecosystem. The defensibility is low because the core value lies in the mathematical approach (NHPP piecewise approximation), which is a standard technique in Operations Research (OR) and can be easily reimplemented by any data scientist or simulation engineer. Frontier labs like OpenAI or Google are unlikely to target this specific niche (ED arrival modeling), but the project faces displacement risk from modern machine learning approaches (e.g., LSTMs, Transformers, or Prophet) which are increasingly favored over traditional Poisson processes for time-series forecasting. Its primary value is as a benchmark for researchers in the discrete event simulation (DES) space. The market for this type of tool is extremely fragmented, typically living within specialized healthcare consultancy scripts or as modules within established simulation software like AnyLogic or Arena.