Qualitative assessment of the complexity of the Karman vortex in the flow past double cylinders based on Shannon entropy

2021 ◽  
Author(s):  
Tao Jia ◽  
Sen Zhang ◽  
Di Gao
Keyword(s):  
Time Series ◽  
Velocity Field ◽  
Shannon Entropy ◽  
Numerical Study ◽  
Moving Average ◽  
Qualitative Assessment ◽  
Autoregressive Moving Average ◽  
Time Series Models ◽  
Conditional Heteroskedasticity ◽  
Karman Vortex

Abstract Numerical simulations of flows past double cylinders under the conditions of different inlet velocities are carried out based on finite element methods. The phenomenon of Karman vortex is observed in the numerical study. Shannon entropy of the velocity field is calculated to quantify the complexity of the velocity field, and the time-evolution of the Shannon entropy data is analyzed by time series models of ARMA (autoregressive moving average) and GARCH (generalized autoregressive conditional heteroskedasticity).

Forecasting performance of time series models on electricity spot markets

2018 ◽  
Vol 12 (4) ◽  
pp. 617-640 ◽  
Author(s):  
Marc Gürtler ◽  
Thomas Paulsen
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Specific Model ◽  
Spot Price ◽  
Autoregressive Moving Average ◽  
Time Series Models ◽  
Testing Time ◽  
Detailed Model ◽  
Content Type ◽  
Forecasting Performance

Purpose Study conditions of empirical publications on time series modeling and forecasting of electricity prices vary widely, making it difficult to generalize results. The key purpose of the present study is to offer a comparison of different model types and modeling conditions regarding their forecasting performance. Design/methodology/approach The authors analyze the forecasting performance of AR (autoregressive), MA (moving average), ARMA (autoregressive moving average) and GARCH (generalized autoregressive moving average) models with and without the explanatory variables, that is, power consumption and power generation from wind and solar. Additionally, the authors vary the detailed model specifications (choice of lag-terms) and transformations (using differenced time series or log-prices) of data and, thereby, obtain individual results from various perspectives. All analyses are conducted on rolling calibrating and testing time horizons between 2010 and 2014 on the German/Austrian electricity spot market. Findings The main result is that the best forecasts are generated by ARMAX models after spike preprocessing and differencing the data. Originality/value The present study extends the existing literature on electricity price forecasting by conducting a comprehensive analysis of the forecasting performance of different time series models under varying market conditions. The results of this study, in general, support the decision-making of electricity spot price modelers or forecasting tools regarding the choice of data transformation, segmentation and the specific model selection.

scholarly journals IDENTIFICATION OF PERIODIC AUTOREGRESSIVE MOVING-AVERAGE TIME SERIES MODELS WITH R

2014 ◽  
Vol 10 (3) ◽  
pp. 358-367
Author(s):  
Hazem I. El Shekh Ahmed ◽  
Raid B. Salha ◽  
Diab I. AL-Awar
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Autoregressive Moving Average ◽  
Time Series Models
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