Time series forecasting using ERNN and QR based on Bayesian model averaging

2017 ◽  
Author(s):  
Augustine Pwasong ◽  
Saratha Sathasivam
Keyword(s):  
Time Series ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Time Series Forecasting

scholarly journals Bayesian Model Averaging: A Unique Model Enhancing Forecasting Accuracy for Daily Streamflow Based on Different Antecedent Time Series

2020 ◽  
Vol 12 (22) ◽  
pp. 9720
Author(s):  
Sungwon Kim ◽  
Meysam Alizamir ◽  
Nam Won Kim ◽  
Ozgur Kisi
Keyword(s):  
Time Series ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Time Series Data ◽  
Model Averaging ◽  
Uncertainty Estimation ◽  
Series Data ◽  
Streamflow Forecasting ◽  
Forecasting Accuracy ◽  
Daily Streamflow

Streamflow forecasting is a vital task for hydrology and water resources engineering, and the different artificial intelligence (AI) approaches have been employed for this purposes until now. Additionally, the forecasting accuracy and uncertainty estimation are the meaningful assignments that need to be recognized. The addressed research investigates the potential of novel ensemble approach, Bayesian model averaging (BMA), in streamflow forecasting using daily time series data from two stations (i.e., Hongcheon and Jucheon), South Korea. Six categories (i.e., M1–M6) of input combination using different antecedent times were employed for streamflow forecasting. The outcomes of BMA model were compared with those of multivariate adaptive regression spline (MARS), M5 model tree (M5Tree), and Kernel extreme learning machines (KELM) models considering four assessment indexes, root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), correlation coefficient (R), and mean absolute error (MAE). The results revealed the superior accuracy of BMA model over three machine learning models in daily streamflow forecasting. Considering RMSE values among the best models during testing phase, the best BMA model (i.e., BMA2) enhanced the forecasting accuracy of MARS1, M5Tree4, and KELM3 models by 5.2%, 5.8%, and 3.4% in Hongcheon station. Additionally, the best BMA model (i.e., BMA1) improved the forecasting accuracy of MARS1, M5Tree1, and KELM1 models by 6.7%, 9.5%, and 3.7% in Jucheon station. In addition, the best BMA models in both stations allowed the uncertainty estimation, and produced higher uncertainty of peak flows compared to that of low flows. As one of the most robust and effective tools, therefore, the BMA model can be successfully employed for streamflow forecasting with different antecedent times.

Bayesian Model Averaging in Time-Series Studies of Air Pollution and Mortality

2007 ◽  
Vol 70 (3-4) ◽  
pp. 311-315 ◽  
Author(s):  
Duncan C. Thomas ◽  
Michael Jerrett ◽  
Nino Kuenzli ◽  
Thomas A. Louis ◽  
Francesca Dominici ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Time Series Studies

scholarly journals Bayesian Model Averaging with Temporal Correlation for Time Series Forecasts

2021 ◽  
Author(s):  
Kosuke Ono
Keyword(s):  
Time Series ◽  
Latent Variables ◽  
Bayesian Model ◽  
Lead Time ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Temporal Correlation ◽  
Lead Times ◽  
Probabilistic Forecasts ◽  
Interval Time Series

AbstractThis study extends Bayesian model averaging (BMA) to a form suitable for time series forecasts. BMA is applied to a three-member ensemble for temperature forecasts with a 1-h interval time series at specific stations. The results of such an application typically have a problematic characteristic. BMA weights assigned to ensemble members fluctuate widely within a few hours because BMA optimizations are independent at each lead time, which is incompatible with the spatiotemporal continuity of meteorological phenomena. To ameliorate this issue, a degree of correlation among different lead times is introduced by the extension of latent variables to lead times adjacent to the target lead time for the calculation of BMA weights and variances. This extension approach stabilizes the BMA weights, improving the performance of deterministic and probabilistic forecasts. Also, an investigation of the effects of this extension technique on the shapes of forecasted probability density functions showed that the extension approach offers advantages in bimodal cases. This extension technique may show promise in other applications to improve the performance of forecasts by BMA.

Bayesian model averaging and identification of structural breaks in time series

2011 ◽  
Vol 43 (26) ◽  
pp. 3805-3818
Author(s):  
Kelvin G. Balcombe ◽  
Iain Fraser ◽  
Abhijit Sharma
Keyword(s):  
Time Series ◽  
Bayesian Model ◽  
Structural Breaks ◽  
Bayesian Model Averaging ◽  
Model Averaging

Selecting Predictors by Using Bayesian Model Averaging in Bridge Models

2012 ◽  
Author(s):  
Lorenzo Bencivelli ◽  
Massimiliano Giuseppe Marcellino ◽  
Gianluca Moretti
Keyword(s):  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Bridge Models
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