ARIMA and ARIMA-ERNN models for prediction of pertussis incidence in mainland China from 2004 to 2021

Wang, Meng; Pan, Jing; Li, Xinghui; Li, Mengying; Liu, Zhixi; Zhao, Qi; Luo, Ling; Chen, Haiping; Chen, Sirui; Jiang, Feng; Zhang, Liping; Wang, Weibing; Wang, Ying

doi:10.1186/s12889-022-13872-9

Cited by 23 publications

(18 citation statements)

References 37 publications

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“…were optimized to yield the accuracy of the REPTree model. The following formulas are used to calculate the metrics: MAE, MSE and RMSE are widely accepted and robust evaluation criterion for error estimation in machine learning algorithms(Teke et al 2015;Son and Kim 2017;Wang et al 2022). MAE, in general computes the average difference between the actual and predicted absolute values on continuous variable data.…”

mentioning

confidence: 99%

WITHDRAWN: Exploring trend and forecasting of temperature and rainfall in Shimla district of Himachal Pradesh, India using non-parametric and bagging REPTree model

Sharma

Sahana

Saha

et al. 2023

Preprint

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The changing pattern of climate variables has caused extreme weather events and severe disasters especially in mountainous regions. Such events have a detrimental impact on resources, environment and society. Thus, it has become imperative to examine trends and forecast of meteorological variables using scientific modeling approach at micro level. This study makes an attempt to examine trend in temperature and rainfall using Modified Mann–Kendall test and Sen’s slope estimator during 1980–2021. A Bagging-REPTree machine learning model was utilized for forecasting temperature and rainfall trend for the next 20 years (2022–2041) to understand the temporal dynamics in Shimla district of Indian Himalayan state. Correlation coefficient (R), mean squared error (MSE), mean absolute error (MAE), and root mean squared error mean (RMSE) performance were determined to assess effectiveness and precision of the model. The findings revealed that the frequency of intense rainfall in the district has increased, especially during the monsoon season (June–September) during 1980–2021. Annual maximum, minimum, and mean temperatures have exhibited significant variability while annual rainfall has shown a decreasing trend. The forecast analysis revealed significant trend for rainfall during monsoon season and increasing trend in the maximum temperature has been observed during summer and winter seasons. The analysis has provided sufficient evidence of variability and uncertainty in the behavior of meteorological variables. The outcome of the study may help in devising suitable adaptation and mitigation strategies to combat the effect of climate change in the hilly regions. The methodology adopted in the study may help in future progression of the research in different geographical regions of Western Himalayas.

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confidence: 99%

WITHDRAWN: Exploring trend and forecasting of temperature and rainfall in Shimla district of Himachal Pradesh, India using non-parametric and bagging REPTree model

Sharma

Sahana

Saha

et al. 2023

Preprint

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“…There are several time-series models, such as ARIMA, exponential smoothing, GARCH, VAR, and prophet models. However, ARIMA is one of the most classic time-series models and has been widely used to predict infectious diseases, including COVID-19[ 50 ], hepatitis B [ 28 ], tuberculosis [ 19 ], human brucellosis [ 51 ], HFMD [ 52 ], and pertussis [ 53 ]. SARIMA is a powerful forecasting tool in public health informatics [ 50 ] that provides an important reference for surveillance and early warning of infectious diseases.…”

Section: Discussionmentioning

confidence: 99%

Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China

Zhao¹,

Zhang²,

Zhang

et al. 2023

BMC Public Health

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Background This study aimed to construct a more accurate model to forecast the incidence of hand, foot, and mouth disease (HFMD) in mainland China from January 2008 to December 2019 and to provide a reference for the surveillance and early warning of HFMD. Methods We collected data on the incidence of HFMD in mainland China between January 2008 and December 2019. The SARIMA, SARIMA-BPNN, and SARIMA-PSO-BPNN hybrid models were used to predict the incidence of HFMD. The prediction performance was compared using the mean absolute error(MAE), mean squared error(MSE), root mean square error (RMSE), mean absolute percentage error (MAPE), and correlation analysis. Results The incidence of HFMD in mainland China from January 2008 to December 2019 showed fluctuating downward trends with clear seasonality and periodicity. The optimal SARIMA model was SARIMA(1,0,1)(2,1,2)[12], with Akaike information criterion (AIC) and Bayesian Schwarz information criterion (BIC) values of this model were 638.72, 661.02, respectively. The optimal SARIMA-BPNN hybrid model was a 3-layer BPNN neural network with nodes of 1, 10, and 1 in the input, hidden, and output layers, and the R-squared, MAE, and RMSE values were 0.78, 3.30, and 4.15, respectively. For the optimal SARIMA-PSO-BPNN hybrid model, the number of particles is 10, the acceleration coefficients c1 and c2 are both 1, the inertia weight is 1, the probability of change is 0.95, and the values of R-squared, MAE, and RMSE are 0.86, 2.89, and 3.57, respectively. Conclusions Compared with the SARIMA and SARIMA-BPNN hybrid models, the SARIMA-PSO-BPNN model can effectively forecast the change in observed HFMD incidence, which can serve as a reference for the prevention and control of HFMD.

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“…where N in the previous formula is the series views value and at the model parameters final value; see Wang et al (2022).…”

Section: The Stages Of Applying Arima Models In Forecastingmentioning

confidence: 99%

Clustering of COVID-19 Multi-Time Series-Based K-Means and PCA With Forecasting

Alaziz

Albayati

El-Bagoury

et al. 2023

International Journal of Data Warehousing and Mining

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The COVID-19 pandemic is one of the current universal threats to humanity. The entire world is cooperating persistently to find some ways to decrease its effect. The time series is one of the basic criteria that play a fundamental part in developing an accurate prediction model for future estimations regarding the expansion of this virus with its infective nature. The authors discuss in this paper the goals of the study, problems, definitions, and previous studies. Also they deal with the theoretical aspect of multi-time series clusters using both the K-means and the time series cluster. In the end, they apply the topics, and ARIMA is used to introduce a prototype to give specific predictions about the impact of the COVID-19 pandemic from 90 to 140 days. The modeling and prediction process is done using the available data set from the Saudi Ministry of Health for Riyadh, Jeddah, Makkah, and Dammam during the previous four months, and the model is evaluated using the Python program. Based on this proposed method, the authors address the conclusions.

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ARIMA and ARIMA-ERNN models for prediction of pertussis incidence in mainland China from 2004 to 2021

Cited by 23 publications

References 37 publications

WITHDRAWN: Exploring trend and forecasting of temperature and rainfall in Shimla district of Himachal Pradesh, India using non-parametric and bagging REPTree model

WITHDRAWN: Exploring trend and forecasting of temperature and rainfall in Shimla district of Himachal Pradesh, India using non-parametric and bagging REPTree model

Research on hand, foot and mouth disease incidence forecasting using hybrid model in mainland China

Clustering of COVID-19 Multi-Time Series-Based K-Means and PCA With Forecasting

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