Time series analysis of wind speed with time‐varying turbulence
By Bradley T. Ewing, Jamie B. Kruse, J. Schroeder. 2006
47 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/e3d0c1b9e2c6c997df2a6bfc735f6d64918f7e56
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Wolfgang Schmid
- Carsten Croonenbroeck
- Daniel Ambach
- Florian Ziel
- Rafal Weron
- Grzegorz Marcjasz
- Bartosz Uniejewski
- Jakub Nowotarski
In Electrical and Electronic Engineering
- Daniel Ambach
- Wolfgang Schmid
- Carsten Croonenbroeck
- Florian Ziel
- Rafal Weron
- Grzegorz Marcjasz
- Bartosz Uniejewski
- Jakub Nowotarski
In Environmental Engineering
Citations in the corpus
- Forecasting wind power – Modeling periodic and non-linear effects under conditional heteroscedasticity (2016, methodology): “With it, we are able to capture the conditionally heteroscedastic behavior in the data, similarly to Ewing et al. (2006) and Ambach and Schmid (2015).”
- GARCH Models (2010, unknown): “…Abramson and Cohen, 2008), daily and monthly temperature measurements (Tol, 1996; Campbell and Diebold, 2005; Romilly, 2005; Huang, Shiu, and Lin, 2008), wind speeds (Ewing, Kruse, and Schroeder, 2006), and atmospheric CO2 concentrations (Hoti, McAleer, and Chan, 2005; McAleer and Chan, 2006).”
- A new high-dimensional time series approach for wind speed, wind direction and air pressure forecasting (2017, background): “The conditional variances of the considered meteorological dependent variables show heteroscedastic behaviour (see Ewing et al., 2006, Ambach and Croonenbroeck, 2016).”