scholarly journals Short Term Forecasting Performances of Classical VAR and Sims-Zha Bayesian VAR Models for Time Series with Collinear Variables and Correlated Error Terms

2015 ◽  
Vol 05 (07) ◽  
pp. 742-753 ◽  
Author(s):  
M. O. Adenomon ◽  
V. A. Michael ◽  
O. P. Evans
Keyword(s):  
Time Series ◽  
Var Models ◽  
Short Term ◽  
Bayesian Var ◽  
Correlated Error ◽  
Error Terms ◽  
Short Term Forecasting

scholarly journals A Comparison of Different Short-Term Macroeconomic Forecasting Models: Evidence from Armenia

2016 ◽  
Vol 5 (2) ◽  
pp. 81-99 ◽  
Author(s):  
Karen Poghosyan
Keyword(s):  
Mean Squared Error ◽  
Small Scale ◽  
Var Models ◽  
Short Term ◽  
Macroeconomic Forecasting ◽  
Bayesian Var ◽  
Out Of Sample ◽  
Competing Models ◽  
Bayesian Factor ◽  
Short Term Forecasting

Abstract We evaluate the forecasting performance of four competing models for short-term macroeconomic forecasting: the traditional VAR, small scale Bayesian VAR, Factor Augmented VAR and Bayesian Factor Augmented VAR models. Using Armenian quarterly actual macroeconomic time series from 1996Q1 – 2014Q4, we estimate parameters of four competing models. Based on the out-of-sample recursive forecast evaluations and using root mean squared error (RMSE) criterion we conclude that small scale Bayesian VAR and Bayesian Factor Augmented VAR models are more suitable for short-term forecasting than traditional unrestricted VAR model.

scholarly journals Forecasting Performances of the Reduced Form VAR and Sims-Zha Bayesian VAR Models when the Multiple Time Series are Jointly Influenced by Collinearity and Autocorrelated Error

2018 ◽  
Author(s):  
Monday Osagie Adenomon ◽  
Benjamin Agboola Oyejola
Keyword(s):  
Time Series ◽  
Data Structure ◽  
Multiple Time ◽  
Multiple Time Series ◽  
Var Models ◽  
Short Term ◽  
Series Length ◽  
Bayesian Var ◽  
Autocorrelated Error ◽  
Different Levels

The goal of VAR or BVAR is the characterization of the dynamics and endogenous relationships among time series. Also the VAR models are known for their applications to forecasting and policy analysis. This paper compare the performance of VAR and Sims-Zha Bayesian VAR models when the multiple time series are jointly influenced by different levels of collinearity and autocorrelation in the short term (T=16, 32, 64 and 128). Five levels (-0.9,-0.5, 0,+0.5,+0.9) of collinearity and autocorrelation were considered and the results from the simulation study revealed that VAR(2) model dominated for no and moderate levels of autocorrelation (-0.5, 0, +0.5) irrespective of the collinearity level except in few cases when T=16. While the BVAR models dominated for high autocorrelation levels (-0.9 and +0.9) irrespective of the collinearity level except in few cases when T=128. The performance of the models varies at different levels of the collinearity and autocorrelated error, and also varies with the short term periods. Furthermore, the values of the RMSE and MAE criteria decrease as a result of increase in the time series length. In conclusion, the performance of the forecasting models depend on the time series data structure and the time series length. It is therefore recommended that the data structure and series length should be considered in using an appropriate model for forecasting.

scholarly journals Improving short-term forecasting during ramp events by means of Regime-Switching Artificial Neural Networks

2011 ◽  
Vol 6 (1) ◽  
pp. 55-58 ◽  
Author(s):  
C. Gallego ◽  
A. Costa ◽  
A. Cuerva
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Regime Switching ◽  
Neural Nets ◽  
Ann Model ◽  
Short Term ◽  
Explanatory Variables ◽  
Ramp Events ◽  
Artificial Neural ◽  
Short Term Forecasting

Abstract. Ramp events are large rapid variations within wind power time series. Ramp forecasting can benefit from specific strategies so as to particularly take into account these shifts in the wind power output dynamic. In the short-term context (characterized by prediction horizons from minutes to a few days), a Regime-Switching (RS) model based on Artificial Neural Nets (ANN) is proposed. The objective is to identify three regimes in the wind power time series: Ramp-up, Ramp-down and No-ramp regime. An on-line regime assessment methodology is also proposed, based on a local gradient criterion. The RS-ANN model is compared to a single-ANN model (without regime discrimination), concluding that the regime-switching strategy leads to significant improvements for one-hour ahead forecasts, mainly due to the improvements obtained during ramp-up events. Including other explanatory variables (NWP outputs, local measurements) during the regime assessment could eventually improve forecasts for further horizons.

Forecasting Performance Comparison by Using Power Transformation between VAR and Bayesian VAR Models

2014 ◽  
Vol 529 ◽  
pp. 621-624
Author(s):  
Syang Ke Kung ◽  
Chi Hsiu Wang
Keyword(s):  
Time Series ◽  
Industrial Production ◽  
Multivariate Time Series ◽  
Performance Comparison ◽  
Power Transformation ◽  
Var Models ◽  
Bayesian Var ◽  
Forecasting Performance ◽  
The U.S ◽  
Forecasting Practice

This article is devoted to examine the performance of power transformation in VAR and Bayesian VAR (BVAR) forecasts, in comparison with log-transformation. The effect of power transformation in multivariate time series model forecasts is still untouched in the literature. We examined the U.S. macroeconomic data from 1960 to 1987 and the Taiwan’s technology industrial production from 1990 to 2000. Our results showed that the power transformation provides outperforming forecasts in both VAR and BVAR models. Moreover, the non-informative prior BAVR with power transformation is the best predictive model and is recommendable to forecasting practice.

scholarly journals ANALYSIS OF METHODS OF INCREASING DATA RELIABILITY FOR PROBLEMS OF SHORT TERM FORECASTING OF NODAL LOAD

2021 ◽  
Vol 2021 (60) ◽  
pp. 51-57
Author(s):  
P.V. Shymaniuk ◽  
◽  
V.O. Miroshnyk ◽  
Keyword(s):  
United States ◽  
Time Series ◽  
Comparative Analysis ◽  
Decomposition Methods ◽  
The United States ◽  
Data Validation ◽  
Clustering Methods ◽  
Short Term ◽  
Northwestern Region ◽  
Short Term Forecasting

A comparative analysis of clustering methods was performed to identify gaps and anomalous values in the data. Data from the northwestern region of the United States were used for evaluation. According to the analysis results, it was found that the use of the DBSCAN method leads to a much smaller number of false positives. An algorithm for two-stage data validation using clustering and time series decomposition methods is proposed. Ref.9, fig. 3, tables 3.

scholarly journals Application of GARCH Forecasting Method in Predicting The Number of Rail Passengers (Thousands of People) in Jabodetabek Region

2022 ◽  
Vol 18 (2) ◽  
pp. 198-223
Author(s):  
Farin Cyntiya Garini ◽  
Warosatul Anbiya
Keyword(s):  
Time Series ◽  
Public Transportation ◽  
Time Series Data ◽  
Series Data ◽  
Analysis Method ◽  
Short Term ◽  
Seasonal Factors ◽  
Forecasting Method ◽  
Short Term Forecasting ◽  
Over Time

PT. Kereta Api Indonesia and PT. KAI Commuter Jabodetabek records time series data in the form of the number of train passengers (thousand people) in Jabodetabek Region in 2011-2020. One of the time series methods that can be used to predict the number of train passengers (thousand people) in Jabodetabek area is ARIMA method. ARIMA or also known as Box-Jenkins time series analysis method is used for short-term forecasting and does not accommodate seasonal factors. If the assumption of residual homoscedasticity is violated, the ARCH / GARCH method can be used, which explicitly models changes in residual variety over time. This study aims to model and forecast the number of train passengers (thousand people) in Jabodetabek area in 2021. Based on data analysis and processing using ARIMA method, the best model is ARIMA (1,1,1) with an AIC value of 2,159.87 and with ARCH / GARCH method, the best model is GARCH (1,1) with an AIC value of 18.314. Forecasting results obtained based on the best model can be used as a reference for related parties in managing and providing public transportation facilities, especially trains.

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