This straightforward process works well in determining a customer’s auto insurance premium. In cases of standard coverage, insurers look for patterns in their extensive claims data to calculate an individual customer’s likelihood of having to make a claim. To operate effectively, insurance companies must prepare for the unknown, predicting the future without much information from events of the past. This comes at a cost, most often covered by insurance, and the speed with which insurers make payments can impact the long-term recovery of a region. To recover from the devastation of a rare disaster like the Northridge Earthquake, individuals and communities need to be able to rebuild quickly while navigating tremendous loss. The cause of this particular quake-one crustal block moving over a second crustal block-produced extremely powerful ground shaking, making it even more destructive. The Northridge Earthquake killed at least 57 people, injured thousands and resulted in tens of billions of dollars in damage. Three benchmark models are used for comparison where results demonstrate the proposed approach leads to significantly better performance while preventing the problem of overlapping quantile estimates.Early on the morning of January 17, 1994, a magnitude 6.7 earthquake rocked California’s San Fernando Valley. Multiple quantiles are estimated to form 20%, 40%, 60% and 80% prediction intervals which are evaluated using the pinball loss function and reliability measures. A numerical case study is conducted using publicly available wind data from the Global Energy Forecasting Competition 2014. This paper analyzes the effectiveness of an approach for nonparametric probabilistic forecasting of wind power that combines support vector machines and nonlinear quantile regression with non-crossing constraints.
CRYSTALMAKER LEHIGH UNIVERSITY FULL
Whereas point forecasting provides a single expected value, probabilistic forecasts provide more information in the form of quantiles, prediction intervals, or full predictive densities. Uncertainty analysis in the form of probabilistic forecasting can provide significant improvements in decision making processes in the smart power gird for better integrating renewable energies such as wind. We conduct experiments to quantitatively and qualitatively show that the identified products and functions have both high coverage and accuracy, compared with a wide spectrum of baselines.
The challenges addressed by the model include the briefness of QAs, linguistic patterns indicating compatibility, and the appropriate fusion of questions and answers. Given a QA pair for a to-be-purchased product, DAN learns to 1) discover complementary products (or functions), and 2) accurately predict the actual compatibility (or satisfiability) of the discovered products (or functions). To allow automatic discovery product compatibility and functionality, we then propose a deep learning model called Dual Attention Network (DAN). We first identify a novel question and answering corpus that is up-to-date regarding product compatibility and functionality information. In this paper, we address two closely related problems: product compatibility analysis and function satisfiability analysis, where the second problem is a generalization of the first problem (e.g., whether a product works with another product can be considered as a special function). Due to the huge number of products available online, it is infeasible to enumerate and test the compatibility and functionality of every product.
Product compatibility and functionality are of utmost importance to customers when they purchase products, and to sellers and manufacturers when they sell products.