Difference between revisions of "User:ARuiz"
From REU@MU
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* Tried to find and come up with new examples for probabilistic forecasting. | * Tried to find and come up with new examples for probabilistic forecasting. | ||
* Read some sections of Gneiting, Tilmann, Fadoua Balabdaoui, and Adrian E. Raftery. "Probabilistic forecasts, calibration and sharpness." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 69.2 (2007): 243-268. | * Read some sections of Gneiting, Tilmann, Fadoua Balabdaoui, and Adrian E. Raftery. "Probabilistic forecasts, calibration and sharpness." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 69.2 (2007): 243-268. | ||
+ | |||
+ | Week 10: | ||
+ | |||
+ | * Met with Dr. Corliss. | ||
+ | * Worked on scoring metrics paper. | ||
+ | * Attended last REU presentations. | ||
+ | * Answered last REU survey. |
Latest revision as of 20:03, 5 August 2016
Week 1:
- Read Superforecasting: The art and science of prediction. Random House, 2015 by Tetlock, Philip, and Dan Gardner.
- Met with mentor for background on the GasDay Labs mission and our research topic.
- Met with mentor for background on the specifics in GasDay Labs models.
- Started researching the scientific literature for examples of uses of probabilistic forecasting.
- Worked with the Brier score metric in order to identify potential flaws.
Week 2:
- Further reading into applications for probabilistic forecasting.
- Implemented basic probabilistic forecasting scoring metrics (pinball, Winkler, Brier).
- Read papers on probabilistic load forecasting.
- Familiarized myself with GasDay Labs' database.
Week 3:
- Attended ethics training
- Started learning about machine learning by implementing simple multivariate linear regression algorithms.
- Read some of the scientific literature for scoring metrics and probabilistic forecast properties.
Week 4:
- Learned about categorical classification machine learning algorithms.
- Read papers about about different strengths and weaknesses in scoring metrics.
- Prepared presentation for GasDay talk.
Week 5:
- learned about regularization for some machine learning models.
- Read uncertainty quantification papers.
- Read papers on probabilistic forecasting.
- Prepared and practiced presentation for REU talk.
Week 6:
- Started learning about the implementation of neural networks.
- Interviewed Matt Thomas for insight on uncertainty quantification.
- Read some of the scientific literature on uncertainty quantification and probabilistic forecasting.
- Met with Dr. Corliss
Week 7:
- Interviewed Dr. Lin on uncertainty quantification.
- Met with Mohammad Saber to discuss probabilistic forecasting and our work.
- Finished writing some of the sections for the REU report.
- Met with Dr. Corliss.
- Attended poster presentation talk for the REU lunch.
- Read some sections of Gneiting, Tilmann, and Adrian E. Raftery. "Strictly proper scoring rules, prediction, and estimation." Journal of the American Statistical Association102.477 (2007): 359-378.
Week 8:
- Met with Dr. Corliss
- Tried to find information on uncertainty quantification.
- Read some sections of Gneiting, Tilmann, Fadoua Balabdaoui, and Adrian E. Raftery. "Probabilistic forecasts, calibration and sharpness." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 69.2 (2007): 243-268.
- Worked on scoring metrics paper.
- Worked on poster presentation.
Week 9:
- Met with Dr. Corliss.
- Prepared and gave seminar presentation at GasDay.
- REU Lunch.
- Gave poster presentation.
- Tried to find and come up with new examples for probabilistic forecasting.
- Read some sections of Gneiting, Tilmann, Fadoua Balabdaoui, and Adrian E. Raftery. "Probabilistic forecasts, calibration and sharpness." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 69.2 (2007): 243-268.
Week 10:
- Met with Dr. Corliss.
- Worked on scoring metrics paper.
- Attended last REU presentations.
- Answered last REU survey.