scholarly journals Temporal trends analysis of tuberculosis morbidity in mainland China from 1997 to 2025 using a new SARIMA-NARNNX hybrid model

BMJ Open ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. e024409 ◽  
Author(s):  
Yongbin Wang ◽  
Chunjie Xu ◽  
Shengkui Zhang ◽  
Zhende Wang ◽  
Li Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Hybrid Model ◽  
Temporal Trends ◽  
Mainland China ◽  
Percentage Error ◽  
Combined Model ◽  
Hybrid Techniques ◽  
Non Linear ◽  
Hidden Neurons ◽  
Autoregressive Neural Network

ObjectiveTuberculosis (TB) remains a major deadly threat in mainland China. Early warning and advanced response systems play a central role in addressing such a wide-ranging threat. The purpose of this study is to establish a new hybrid model combining a seasonal autoregressive integrated moving average (SARIMA) model and a non-linear autoregressive neural network with exogenous input (NARNNX) model to understand the future epidemiological patterns of TB morbidity.MethodsWe develop a SARIMA-NARNNX hybrid model for forecasting future levels of TB incidence based on data containing 255 observations from January 1997 to March 2018 in mainland China, and the ultimate simulating and forecasting performances were compared with the basic SARIMA, non-linear autoregressive neural network (NARNN) and error-trend-seasonal (ETS) approaches, as well as the SARIMA-generalised regression neural network (GRNN) and SARIMA-NARNN hybrid techniques.ResultsIn terms of the root mean square error, mean absolute error, mean error rate and mean absolute percentage error, the identified best-fitting SARIMA-NARNNX combined model with 17 hidden neurons and 4 feedback delays had smaller values in both in-sample simulating scheme and the out-of-sample forecasting scheme than the preferred single SARIMA(2,1,3)(0,1,1)12model, a NARNN with 19 hidden neurons and 6 feedback delays and ETS(M,A,A), and the best-performing SARIMA-GRNN and SARIMA-NARNN models with 32 hidden neurons and 6 feedback delays. Every year, there was an obvious high-risk season for the notified TB cases in March and April. Importantly, the epidemic levels of TB from 2006 to 2017 trended slightly downward. According to the projection results from 2018 to 2025, TB incidence will continue to drop by 3.002% annually but will remain high.ConclusionsThe new SARIMA-NARNNX combined model visibly outperforms the other methods. This hybrid model should be used for forecasting the long-term epidemic patterns of TB, and it may serve as a beneficial and effective tool for controlling this disease.

scholarly journals Predictive study of tuberculosis incidence by time series method and Elman neural network in Kashgar, China

BMJ Open ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. e041040
Author(s):  
Yanling Zheng ◽  
Xueliang Zhang ◽  
Xijiang Wang ◽  
Kai Wang ◽  
Yan Cui
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Hybrid Method ◽  
Prevention And Control ◽  
Temporal Trends ◽  
Percentage Error ◽  
Elman Neural Network ◽  
Prediction Analysis ◽  
And Control

ObjectivesKashgar, located in Xinjiang, China has a high incidence of tuberculosis (TB) making prevention and control extremely difficult. In addition, there have been very few prediction studies on TB incidence here. We; therefore, considered it a high priority to do prediction analysis of TB incidence in Kashgar, and so provide a scientific reference for eventual prevention and control.DesignTime series study.Setting Kashgar, ChinaKashgar, China.MethodsWe used a single Box-Jenkins method and a Box-Jenkins and Elman neural network (ElmanNN) hybrid method to do prediction analysis of TB incidence in Kashgar. Root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to measure the prediction accuracy.ResultsAfter careful analysis, the single autoregression (AR) (1, 2, 8) model and the AR (1, 2, 8)-ElmanNN (AR-Elman) hybrid model were established, and the optimal neurons value of the AR-Elman hybrid model is 6. In the fitting dataset, the RMSE, MAE and MAPE were 6.15, 4.33 and 0.2858, respectively, for the AR (1, 2, 8) model, and 3.78, 3.38 and 0.1837, respectively, for the AR-Elman hybrid model. In the forecasting dataset, the RMSE, MAE and MAPE were 10.88, 8.75 and 0.2029, respectively, for the AR (1, 2, 8) model, and 8.86, 7.29 and 0.2006, respectively, for the AR-Elman hybrid model.ConclusionsBoth the single AR (1, 2, 8) model and the AR-Elman model could be used to predict the TB incidence in Kashgar, but the modelling and validation scale-dependent measures (RMSE, MAE and MAPE) in the AR (1, 2, 8) model were inferior to those in the AR-Elman hybrid model, which indicated that the AR-Elman hybrid model was better than the AR (1, 2, 8) model. The Box-Jenkins and ElmanNN hybrid method therefore can be highlighted in predicting the temporal trends of TB incidence in Kashgar, which may act as the potential for far-reaching implications for prevention and control of TB.

scholarly journals Seasonality and trend prediction of scarlet fever incidence in mainland China from 2004 to 2018 using a hybrid SARIMA-NARX model

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6165 ◽  
Author(s):  
Yongbin Wang ◽  
Chunjie Xu ◽  
Zhende Wang ◽  
Juxiang Yuan
Keyword(s):  
Root Mean Square ◽  
Scarlet Fever ◽  
Current Trend ◽  
Mainland China ◽  
Public Health Issue ◽  
Percentage Error ◽  
Mean Square ◽  
Trend Prediction ◽  
Hidden Neurons ◽  
Nonlinear Autoregressive

Background Scarlet fever is recognized as being a major public health issue owing to its increase in notifications in mainland China, and an advanced response based on forecasting techniques is being adopted to tackle this. Here, we construct a new hybrid method incorporating seasonal autoregressive integrated moving average (SARIMA) with a nonlinear autoregressive with external input(NARX) to analyze its seasonality and trend in order to efficiently prevent and control this re-emerging disease. Methods Four statistical models, including a basic SARIMA, basic nonlinear autoregressive (NAR) method, traditional SARIMA-NAR and new SARIMA-NARX hybrid approaches, were developed based on scarlet fever incidence data between January 2004 and July 2018 to evaluate its temporal patterns, and their mimic and predictive capacities were compared to discover the optimal using the mean absolute percentage error, root mean square error, mean error rate, and root mean square percentage error. Results The four preferred models identified were comprised of the SARIMA(0,1,0)(0,1,1)12, NAR with 14 hidden neurons and five delays, SARIMA-NAR with 33 hidden neurons and five delays, and SARIMA-NARX with 16 hidden neurons and 4 delays. Among which presenting the lowest values of the aforementioned indices in both simulation and prediction horizons is the SARIMA-NARX method. Analyses from the data suggested that scarlet fever was a seasonal disease with predominant peaks of summer and winter and a substantial rising trend in the scarlet fever notifications was observed with an acceleration of 9.641% annually, particularly since 2011 with 12.869%, and moreover such a trend will be projected to continue in the coming year. Conclusions The SARIMA-NARX technique has the promising ability to better consider both linearity and non-linearity behind scarlet fever data than the others, which significantly facilitates its prevention and intervention of scarlet fever. Besides, under current trend of ongoing resurgence, specific strategies and countermeasures should be formulated to target scarlet fever.

scholarly journals Hybrid methodology for tuberculosis incidence time-series forecasting based on ARIMA and a NAR neural network

2017 ◽  
Vol 145 (6) ◽  
pp. 1118-1129 ◽  
Author(s):  
K. W. WANG ◽  
C. DENG ◽  
J. P. LI ◽  
Y. Y. ZHANG ◽  
X. Y. LI ◽  
...  
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Prevention And Control ◽  
Time Series Data ◽  
Arima Model ◽  
Public Health Problem ◽  
Series Data ◽  
Percentage Error ◽  
And Control

SUMMARYTuberculosis (TB) affects people globally and is being reconsidered as a serious public health problem in China. Reliable forecasting is useful for the prevention and control of TB. This study proposes a hybrid model combining autoregressive integrated moving average (ARIMA) with a nonlinear autoregressive (NAR) neural network for forecasting the incidence of TB from January 2007 to March 2016. Prediction performance was compared between the hybrid model and the ARIMA model. The best-fit hybrid model was combined with an ARIMA (3,1,0) × (0,1,1)12 and NAR neural network with four delays and 12 neurons in the hidden layer. The ARIMA-NAR hybrid model, which exhibited lower mean square error, mean absolute error, and mean absolute percentage error of 0·2209, 0·1373, and 0·0406, respectively, in the modelling performance, could produce more accurate forecasting of TB incidence compared to the ARIMA model. This study shows that developing and applying the ARIMA-NAR hybrid model is an effective method to fit the linear and nonlinear patterns of time-series data, and this model could be helpful in the prevention and control of TB.

scholarly journals Automatic time series forecasting using nonlinear autoregressive neural network model with exogenous input

2021 ◽  
Vol 10 (5) ◽  
pp. 2836-2844
Author(s):  
Hermansah Hermansah ◽  
Dedi Rosadi ◽  
Abdurakhman Abdurakhman ◽  
Herni Utami
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Model ◽  
Neural Network Model ◽  
Mean Squared Error ◽  
Real Data ◽  
Hidden Neurons ◽  
Nonlinear Autoregressive ◽  
Automatic Forecasting ◽  
Autoregressive Neural Network

This study aims to determine an automatic forecasting method of univariate time series, using the nonlinear autoregressive neural network model with exogenous input (NARX). In this automatic setting, users only need to supply the input of time series. Then, an automatic forecasting algorithm sets up the appropriate features, estimate the parameters in the model, and calculate forecasts, without the users’ intervention. The algorithm method used include preprocessing, tests for trends, and the application of first differences. The time series were tested for seasonality, and seasonal differences were obtained from a successful analysis. These series were also linearly scaled to [−1, +1]. The autoregressive lags and hidden neurons were further selected through the stepwise and optimization algorithms, respectively. The 20 NARX models were fitted with different random starting weights, and the forecasts were combined using the ensemble operator, in order to obtain the final product. This proposed method was applied to real data, and its performance was compared with several available automatic models in the literature. The forecasting accuracy was also measured by mean squared error (MSE) and mean absolute percent error (MAPE), and the results showed that the proposed method outperformed the other automatic models.

scholarly journals Electricity Price Forecasting using a Hybrid of Neural Network and Genetic Algorithm

2019 ◽  
Vol 8 (12S2) ◽  
pp. 675-679
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Hybrid Model ◽  
Electricity Price ◽  
Energy Market ◽  
Percentage Error ◽  
Price Forecasting ◽  
Electricity Price Forecasting ◽  
The Neural Network ◽  
The Individual

Electricity price forecasting has gained a reputation for its importance in the deregulated energy market. The forecast process can be complicated as it depends on many elements. This paper proposes a hybrid of a neural network with a genetic algorithm for the electricity price forecasting. The Ontario energy market is select as the tested market for this model. The features for the neural network input are the actual historical demand and actual Hourly Ontario Energy Price (HOEP). The genetic algorithms help to select the number of features and to optimize the parameters of the neural network. This hybrid model helps to improve the accuracy of the forecasted price when comparing with the accuracy of the individual neural network itself. The mean absolute percentage error has represented the accuracy of the hybrid model, and it is used as a benchmark of the proposed hybrid model with other models.

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