A time series forecasting approach for queue wait-time prediction

Abstract: Waiting in queues is an unavoidable part of life, and not knowing how long the wait is going to be can be a big source of anxiety. In an attempt to mitigate this, and to be able to manage their queues, companies often try to estimate wait-times. This is especially important in healthcare, since the patients are most likely already under some distress. In this thesis the performance of two different machine learning (ML) approaches and a simulation approach were compared on the wait-time prediction problem in a digital healthcare setting. Additionally, a combination approach was implemented, combining the best ML model with the simulation approach. The ML approaches used historical data of the patient queue in order to produce a model which could predict the wait-time for new patients joining the queue. The simulation algorithm mimics the queue in a virtual environment and simulates time moving forward until the new patient joining the queue is assigned a clinician, thus producing a wait-time estimation. The combination approach used the wait-time estimations produced by the simulation algorithm as an additional input feature for the best ML model. A Temporal Convolutional Network (TCN) model and a Long Short-Term Memory network (LSTM) model were implemented and represented the sequence modeling ML approach. A Random Forest Regressor (RF) model and a Support Vector Regressor (SVR) model were implemented and represented the traditional ML approach. In order to introduce the temporal dimension to the traditional ML approach, the exponential smoothing preprocessing technique was applied. The results indicated that there was a statistically significant difference between all models. The TCN model and the simulation algorithm had the lowest Mean Square Error (MSE) of all individual models. Both sequence modeling models had lower MSE compared to both of the traditional ML models. The combination model had the lowest MSE of all, adopting the best performance traits from both the ML approach and the simulation approach. However, the combination model is the most complex, and thus requires the most maintenance. Due to the limitations in the study, no single approach can be concluded as optimal. However, the results suggest that the sequence modeling approach is a viable option in wait-time prediction, and is recommended for future research or applications.