The comparative analysis of SARIMA, Facebook Prophet, and LSTM for road traffic injury prediction in Northeast China

Abstract

ObjectiveThis cross-sectional research aims to develop reliable predictive short-term prediction models to predict the number of RTIs in Northeast China through comparative studies.MethodologySeasonal auto-regressive integrated moving average (SARIMA), Long Short-Term Memory (LSTM), and Facebook Prophet (Prophet) models were used for time series prediction of the number of RTIs inpatients. The three models were trained using data from 2015 to 2019, and their prediction accuracy was compared using data from 2020 as a test set. The parameters of the SARIMA model were determined using the autocorrelation function (ACF) and the partial autocorrelation function (PACF). The LSTM uses linear as the activation function, the mean square error (MSE) as the loss function and the Adam optimizer to construct the model, while the Prophet model is built on the Python platform. The root mean squared error (RMSE), mean absolute error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the predictive performance of the model.FindingsIn this research, the LSTM model had the highest prediction accuracy, followed by the Prophet model, and the SARIMA model had the lowest prediction accuracy. The trend in medical expenditure of RTIs inpatients overlapped highly with the number of RTIs inpatients.ConclusionBy adjusting the activation function and optimizer, the LSTM predicts the number of RTIs inpatients more accurately and robustly than other models. Compared with other models, LSTM models still show excellent prediction performance in the face of data with seasonal and drastic changes. The LSTM can provide a better basis for planning and management in healthcare administration.ImplicationThe results of this research show that it is feasible to accurately forecast the demand for healthcare resources with seasonal distribution using a suitable forecasting model. The prediction of specific medical service volumes will be an important basis for medical management to allocate medical and health resources.

Time series models in prediction of severe fever with thrombocytopenia syndrome cases in Shandong province, China

“…Since its introduction, the Prophet model has been widely used in medical research and other areas, such as COVID-19 ( Battineni et al., 2020 ; Khayyat et al., 2021 ; Satrio et al., 2021 ), hand, foot and mouth disease ( Xie et al. ), air pollution ( Shen et al., 2020 ), road traffic injuries ( Feng et al., 2022 ), etc., and has shown good prediction performance. In this study, the Prophet model performed better when the actual number of cases was higher or lower (e.g., in January, June, and November).…”

Trend analysis and prediction of gonorrhea in mainland China based on a hybrid time series model

“…SARIMA and ANN models have been successfully used to fit and predict time series data in a variety of fields [ 18 – 22 ]. The SARIMA model can fit seasonal fluctuations well, but the fitting accuracy is poor for nonlinear components of TS data [ 23 ], while the LSTM model can compensate for this deficiency well, but another problem is that the mandatory fitting of seasonal fluctuations using a single LSTM model over a longer period increases the risk of overfitting, so a hybrid SARIMA-LSTM model was used to solve the accuracy problem of nonlinear fitting and simulate seasonal fluctuations at the same time [ 24 ].…”

Mushroom poisoning outbreaks in Guizhou Province, China: a prediction study using SARIMA and Prophet models

“…The Ljung–Box (LBQ) test was used to determine whether the series is a white noise series in the SARIMA model. A fitted model was formed if the P -value of the white noise test was greater than the significance level ( P > 0.05), indicating that the series is white noise 21 , 23 , 24 . Based on the parameters RMSEA and measured data between January and June 2022, the SARIMA and Prophet models were assessed.…”