scholarly journals Bayesian Time‐Series Models in Single Case Experimental Designs: A Tutorial for Trauma Researchers

2020 ◽  
Vol 33 (6) ◽  
pp. 1144-1153
Author(s):  
Prathiba Natesan Batley ◽  
Ateka A. Contractor ◽  
Stephanie V. Caldas
Keyword(s):  
Time Series ◽  
Single Case ◽  
Experimental Designs ◽  
Time Series Models ◽  
Bayesian Time Series

Fitting Bayesian Models for Single-Case Experimental Designs

Methodology ◽  
2019 ◽  
Vol 15 (4) ◽  
pp. 147-156 ◽  
Author(s):  
Prathiba Natesan
Keyword(s):  
Time Series ◽  
Single Case ◽  
Interrupted Time Series ◽  
Experimental Designs ◽  
Short Time Series ◽  
Strong Basis ◽  
Steep Learning Curve ◽  
Methodological Rigor ◽  
Parametric Analyses ◽  
Short Time

Abstract. Single-case experimental designs (SCEDs) are interrupted time-series designs that have recently gained recognition as being able to provide a strong basis for establishing intervention effect. Typically, SCED data are short time series and autocorrelated, which renders maximum likelihood and parametric analyses inadequate for data analysis, respectively. Although Bayesian methods overcome these challenges, most practitioners do not use Bayesian estimation because of: (a) its steep learning curve, (b) lack of Bayesian training, and (c) lack of knowledge of Bayesian software solutions. This study demonstrates two Bayesian interrupted time-series models using freeware programs R and JAGS. Practitioners could modify these codes and run them for their own data by changing the values in the codes where indicated. Providing practitioners with such tools to facilitate their analysis is one way to improve methodological rigor in applied research.

Predicting system degradation using Bayesian time series models

2021 ◽  
Author(s):  
Jerzy Baranowski ◽  
Waldemar Bauer ◽  
Nataliia Kashpruk ◽  
Marta Zagorowska
Keyword(s):  
Time Series ◽  
Time Series Models ◽  
Bayesian Time Series

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

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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