Revision as of 00:31, 9 July 2016 by ARuiz
- 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.
- 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.
- Attended ethics training
- Started learning about machine learning by implementing simple multivariate linear regression algorithms.
- Learned about categorical classification machine learning algorithms.
- Read papers about about different strengths and weaknesses in scoring metrics.
- Prepared presentation for GasDay talk.
- learned about regularization for some machine learning models.
- Read uncertainty quantification papers.
- Read papers on probabilistic forecasting.
- Prepared and practiced presentation for REU talk.
- 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.