From: Forecasting patient flows with pandemic induced concept drift using explainable machine learning
Study | Year | Forecast periods (days) | Features | Best algorithm | MAPE |
---|---|---|---|---|---|
Boyle et al. [5] | 2012 | 1 | 4 | ARIMA, ES, OLS | 7.0% |
Xu et al. [51] | 2013 | 1 | 7 | ANN | 6.8%–7.3% |
Marcilio et al. [29] | 2013 | 7 | 15 | GLM | 7.6% |
 | 30 |  | GLM | 9.7% | |
Xu et al. [52] | 2016 | 1 | 31 | ARIMA-LR | 6.5% |
 | 7 |  | ARIMA-LR | 9.6% | |
Calegari et al. [7] | 2016 | 1 | 5 | SES | 2.9% |
 | 7 |  | SES | 10.7% | |
 | 14 |  | SES | 10.7% | |
 | 21 |  | SES | 11.4% | |
 | 30 |  | SES | 11.7% | |
Navares et al. [32] | 2018 | 1 | 13 | ARIMA | 8.1%–12.3% |
Whitt and Zhang [49] | 2019 | 1 | 57 | SARIMAX | 8.4% |
Rocha and Rodrigues [39] | 2021 | 1 | 15 | LSTM | 4.2% |
Sudarshan et al. [42] | 2021 | 3 | 10 | CNN | 9.2% |
 | 7 |  | LSTM | 8.9% | |
Vollmer et al. [47] | 2021 | 1 | 30 | Stacking | 6.8%–8.6% |
Harrou et al. [21] | 2022 | 1 | 7 | DBN | 4.1% |
Zhang et al. [53] | 2022 | 1 | 29 | SVR | 8.8% |
Petsis et al. [37] | 2022 | 1 | 38 | XGBoost | 6.5% |
 | 2 |  | XGBoost | 6.9% |