User:ARuiz

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.