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.

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.
• Read uncertainty quantification papers
• Finally finished reading Gneiting et al. (2014) paper on probabilistic forecasting.
• Interviewed engineers for insight on uncertainty quantification.