Applying time series models to estimate time lags between sap flux and micro-meteorological factors

Abstract BackgroundMany studies have compared the performance of time-series models in predicting pulmonary tuberculosis (PTB). Few studies regarding the role of meteorological factors in predicting PTB are available. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting pulmonary tuberculosis (PTB).MethodsWe collected the monthly number of PTB cases registered in three cities of China from 2005 to 2018, and data of six meteorological factors in the same period. We constructed three time-series models, including the autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and the recurrent neural network (RNN) model. The construction of the ARIMA model did not incorporate meteorological factors, while the construction of ARIMAX and RNN models incorporated meteorological factors. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to compare the performance of models in predicting the monthly number of PTB cases in 2018.ResultsBoth the cross-correlation analysis and spearman rank correlation test showed that PTB was related to meteorological factors in the three cities. The prediction performance of both ARIMA and RNN models was improved after incorporating the meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.536%, 11.957%, and 12.360% in Xuzhou, 15.568%, 11.155%, and 14.087% in Nantong, and 9.700%, 9.660% and 12.501% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956 and 34.785 in Xuzhou, 34.073, 25.884 and 31.828 in Nantong, and 19.545, 19.026 and 26.019 in Wuxi, respectively.ConclusionsOur study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration may increase the accuracy of time series models in predicting PTB.

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Effectiveness of cascading time series models based on meteorological factors in improving health risk prediction

Environmental Science and Pollution Research ◽

10.1007/s11356-021-16372-2 ◽

2021 ◽

Author(s):

Shuopeng Jia ◽

Weibin She ◽

Zhipeng Pi ◽

Buying Niu ◽

Jinhua Zhang ◽

...

Keyword(s):

Time Series ◽

Health Risk ◽

Risk Prediction ◽

Meteorological Factors ◽

Time Series Models

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Comparing the performance of time series models with or without meteorological factors in predicting incident pulmonary tuberculosis in eastern China

Infectious Diseases of Poverty ◽

10.1186/s40249-020-00771-7 ◽

2020 ◽

Vol 9 (1) ◽

Author(s):

Zhong-Qi Li ◽

Hong-Qiu Pan ◽

Qiao Liu ◽

Huan Song ◽

Jian-Ming Wang

Keyword(s):

Time Series ◽

Pulmonary Tuberculosis ◽

Prediction Models ◽

Meteorological Factors ◽

Eastern China ◽

Arima Model ◽

Rank Correlation ◽

Percentage Error ◽

Time Series Models ◽

Cross Correlation Analysis

Abstract Background Many studies have compared the performance of time series models in predicting pulmonary tuberculosis (PTB), but few have considered the role of meteorological factors in their prediction models. This study aims to explore whether incorporating meteorological factors can improve the performance of time series models in predicting PTB. Methods We collected the monthly reported number of PTB cases and records of six meteorological factors in three cities of China from 2005 to 2018. Based on this data, we constructed three time series models, including an autoregressive integrated moving average (ARIMA) model, the ARIMA with exogenous variables (ARIMAX) model, and a recurrent neural network (RNN) model. The ARIMAX and RNN models incorporated meteorological factors, while the ARIMA model did not. The mean absolute percentage error (MAPE) and root mean square error (RMSE) were used to evaluate the performance of the models in predicting PTB cases in 2018. Results Both the cross-correlation analysis and Spearman rank correlation test showed that PTB cases reported in the study areas were related to meteorological factors. The predictive performance of both the ARIMA and RNN models was improved after incorporating meteorological factors. The MAPEs of the ARIMA, ARIMAX, and RNN models were 12.54%, 11.96%, and 12.36% in Xuzhou, 15.57%, 11.16%, and 14.09% in Nantong, and 9.70%, 9.66%, and 12.50% in Wuxi, respectively. The RMSEs of the three models were 36.194, 33.956, and 34.785 in Xuzhou, 34.073, 25.884, and 31.828 in Nantong, and 19.545, 19.026, and 26.019 in Wuxi, respectively. Conclusions Our study revealed a possible link between PTB and meteorological factors. Taking meteorological factors into consideration increased the accuracy of time series models in predicting PTB, and the ARIMAX model was superior to the ARIMA and RNN models in study settings.

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Time-Series Models in Marketing: Some Recent Developments

Marketing ZFP ◽

10.15358/0344-1369-2010-jrm-1-24 ◽

2010 ◽

Vol 32 (JRM 1) ◽

pp. 24-29

Author(s):

Marnik G. Dekimpe ◽

Dominique M. Hanssens

Keyword(s):

Time Series ◽

Time Series Models ◽

Recent Developments

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A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA

MALAYSIAN JOURNAL OF COMPUTING ◽

10.24191/mjoc.v5i1.6760 ◽

2020 ◽

Vol 5 (1) ◽

pp. 374

Author(s):

Pauline Jin Wee Mah ◽

Nur Nadhirah Nanyan

Keyword(s):

Time Series ◽

Palm Oil ◽

Moving Average ◽

Forecast Accuracy ◽

Peninsular Malaysia ◽

Time Series Models ◽

Crude Palm Oil ◽

Univariate Time Series ◽

Import And Export ◽

Bivariate Time Series

The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA emerged the best model based on the RMSE value. When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

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