What benefits are to be derived from improved farm program planning approaches? – The role of time series models and stochastic optimization

2007 ◽  
Vol 95 (1-3) ◽  
pp. 11-27 ◽  
Author(s):  
Oliver Musshoff ◽  
Norbert Hirschauer
Keyword(s):  
Time Series ◽  
Stochastic Optimization ◽  
Program Planning ◽  
Time Series Models ◽  
Farm Program ◽  
Planning Approaches

scholarly journals Exploring the role of meteorological factors in predicting incident pulmonary tuberculosis: A time-series study in eastern China

2020 ◽  
Author(s):  
Zhongqi Li ◽  
Hongqiu Pan ◽  
Qiao Liu ◽  
Huan Song ◽  
Jianming Wang
Keyword(s):  
Time Series ◽  
Pulmonary Tuberculosis ◽  
Meteorological Factors ◽  
Eastern China ◽  
Arima Model ◽  
Rank Correlation ◽  
Percentage Error ◽  
Time Series Models ◽  
Cross Correlation Analysis

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.

The role of parental self-regulation and household chaos in parent-toddler interactions: A time-series study.

2020 ◽  
Author(s):  
Sanne B. Geeraerts ◽  
Joyce Endendijk ◽  
Kirby Deater-Deckard ◽  
Jorg Huijding ◽  
Marike H. F. Deutz ◽  
...  
Keyword(s):  
Time Series ◽  
Self Regulation ◽  
Household Chaos ◽  
Time Series Study ◽  
Series Study

Time-Series Models in Marketing: Some Recent Developments

Marketing ZFP ◽  
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

Pembinaan Akhlak Narapidana di Pondok Pesantren At- Taubah Lembaga Pemasyarakatan Cianjur

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Arip Hidayatulloh ◽  
Herman Herman ◽  
Asep Iwan Setiawan
Keyword(s):  
Program Planning ◽  
Boarding School ◽  
Class Ii ◽  
Correctional Institution ◽  
Descriptive Method ◽  
Moral Guidance

Penelitian ini bertujuan untuk mengetahui peran pondok pesantren At Taubah di Lembaga Pemasyarakatan Klas II B Cianjur mengenai pembinaan Akhlak Narapidana. Dengan memahami perencanaan program dan pelaksanaan pembinaan akhlak narapidana. Penelitian ini menggunakan Metode deskriptif. Hasil penelitian menunjukan bahwa Peran pesantren At Taubah yang berada di LAPAS Klas II B Cianjur dalam membina akhlak narapidana yaitu memberikan sumbangsih besar terhadap pembinaan yang secara umum merupakan tugas dari Lembaga Pemasyarakatan. Hal ini terlihat dari kegiatan yang dilaksanakan oleh pesantren yang begitu terorganisir.   This study aims to determine the role of the At Taubah Islamic boarding school in Cianjur Class II B Correctional Institution regarding the Moral Guidance of Prisoners. By understanding program planning and the implementation of moral guidance for prisoners. This research uses a descriptive method. The results showed that the role of the At Taubah Islamic Boarding School in the Class II B Classroom in Cianjur in building up the morals of prisoners is to contribute greatly to coaching which is generally the task of the Penitentiary. This can be seen from the activities carried out by pesantren that are so organized.

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

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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