We read every piece of feedback, and take your input very seriously.
To see all available qualifiers, see our documentation.
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Conclusions from June 15, 2021 Preseason Forecast Meeting: • Bootstrap CI versus prediction interval on the forecast? o The decision was made to go with the prediction interval. • Use model averaging (what models to include) or just go with the top model based on a performance metric as AICc, MASE etc. and not use model averaging? o The decision was made to go with model averaging but to weight the models based on a different metric than AIC (e.g., MAPE_LOOCV, MAPE_one_step_ahead, wMAPE but weight the historical years in a decreasing fashion) o Additional references (thanks to Rich): https://www.cs.cmu.edu/~schneide/tut5/node42.html https://www.adfg.alaska.gov/static/applications/dcfnewsrelease/1232415165.pdf https://www.stat.umn.edu/geyer/5421/slides/glmbb.html https://uoftcoders.github.io/rcourse/lec09-model-selection.html https://cran.r-project.org/web/packages/AICcmodavg/vignettes/AICcmodavg.pdf https://rdrr.io/cran/MuMIn/man/model.avg.html https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecm.1309 https://theoreticalecology.wordpress.com/2018/05/14/model-averaging-in-ecology-a-review-of-bayesian-information-theoretic-and-tactical-approaches-for-predictive-inference/ o Rich and Sara will see if they can find any support for one performance metric over another. • Move to just the 20 m ISTI variable for the SECM variables for the 2022 forecast? o The decision was made to just stick with ISTI_20m_MJJ variable as a possible variable in the models; The other ISTI variables will not be included as possible variables in the model averaging process for the 2022 preseason forecast. • Logical set of environmental variables (space/time) to assess?—25 models currently in the document o Reduced set of variables that includes:
The text was updated successfully, but these errors were encountered:
No branches or pull requests
Conclusions from June 15, 2021 Preseason Forecast Meeting:
• Bootstrap CI versus prediction interval on the forecast?
o The decision was made to go with the prediction interval.
• Use model averaging (what models to include) or just go with the top model based on a performance metric as AICc, MASE etc. and not use model averaging?
o The decision was made to go with model averaging but to weight the models based on a different metric than AIC (e.g., MAPE_LOOCV, MAPE_one_step_ahead, wMAPE but weight the historical years in a decreasing fashion)
o Additional references (thanks to Rich):
https://www.cs.cmu.edu/~schneide/tut5/node42.html
https://www.adfg.alaska.gov/static/applications/dcfnewsrelease/1232415165.pdf
https://www.stat.umn.edu/geyer/5421/slides/glmbb.html
https://uoftcoders.github.io/rcourse/lec09-model-selection.html
https://cran.r-project.org/web/packages/AICcmodavg/vignettes/AICcmodavg.pdf
https://rdrr.io/cran/MuMIn/man/model.avg.html
https://esajournals.onlinelibrary.wiley.com/doi/10.1002/ecm.1309
https://theoreticalecology.wordpress.com/2018/05/14/model-averaging-in-ecology-a-review-of-bayesian-information-theoretic-and-tactical-approaches-for-predictive-inference/
o Rich and Sara will see if they can find any support for one performance metric over another.
• Move to just the 20 m ISTI variable for the SECM variables for the 2022 forecast?
o The decision was made to just stick with ISTI_20m_MJJ variable as a possible variable in the models; The other ISTI variables will not be included as possible variables in the model averaging process for the 2022 preseason forecast.
• Logical set of environmental variables (space/time) to assess?—25 models currently in the document
o Reduced set of variables that includes:
The text was updated successfully, but these errors were encountered: