Proceedings of the 2010 Conference on Intelligent Data Understanding, CIDU 2010, October 5-6, 2010, Mountain View, California, USA
نویسندگان
چکیده
Climate models are complex mathematical models designed by meteorologists, geophysicists, and climate scientists to simulate and predict climate. Given temperature predictions from the top 20 climate models worldwide, and over 100 years of historical temperature data, we track the changing sequence of which model currently predicts best. We use an algorithm due to Monteleoni and Jaakkola that models the sequence of observations using a hierarchical learner, based on a set of generalized Hidden Markov Models (HMM), where the identity of the current best climate model is the hidden variable. The transition probabilities between climate models are learned online, simultaneous to tracking the temperature predictions. On historical data, our online learning algorithm’s average prediction loss nearly matches that of the best performing climate model in hindsight. Moreover its performance surpasses that of the average model prediction, which was the current state-of-the-art in climate science, the median prediction, and least squares linear regression. We also experimented on climate model predictions through the year 2098. Simulating labels with the predictions of any one climate model, we found significantly improved performance using our online learning algorithm with respect to the other climate models, and techniques.
منابع مشابه
Proceedings of the 2011 Conference on Intelligent Data Understanding, CIDU 2011, October 19-21, 2011, Mountain View, California, USA
Energy production, distribution, and consumption play a critical role in the sustainability of the planet and its natural resources. Electric power systems have been going through major changes that are aimed to make the energy infrastructure “smarter”, scalable, and more efficient. These new generation of smart energy grids need novel computational algorithms for supporting generation of power...
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