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- Saahil Shenoy, Dimitry M. Gorinevsky
- ACC
- 2014

Load forecasting of energy demand is usually focused on mean values in related statistical models and ignores rare peak events. This paper provides Extreme Value Theory analysis of the peak events in electrical power load demand. It estimates risk of the peak events by combining forecast of the mean with extreme value modeling of distribution tail. The… (More)

This paper develops a method for building nonparametric stochastic models of multivariate distributions from large data sets. The motivation is stochastic optimization based on time series forecasting models. The proposed non-parametric stochastic modeling approach is based on multiple quantile regressions with inter-quantile smoothing. The models are built… (More)

- Saahil Shenoy, Dimitry M. Gorinevsky
- IEEE Signal Processing Letters
- 2015

This letter develops a method for estimating trends of extreme events statistics across multiple time periods. Some of the periods might have no extreme events and some might have much data. The extreme event distribution is modeled with a Pareto or exponential tail. The method requires selecting an extreme event threshold and then solving two convex… (More)

The paper considers stochastic optimization of the electricity procurement in the day-ahead power market. The novelty is in addressing the random errors of time series forecasting of electrical power loads and prices in the procurement. This problem is currently important because of the increased random variability in the power grid that is caused by… (More)

- Saahil Shenoy, Dimitry M. Gorinevsky
- CDC
- 2014

This paper develops a statistical modeling and estimation approach combining robust regression and long tail estimation. The approach can be considered as a generalization of Huber regression in robust statistics. A mixture of asymmetric Laplace and Gaussian distributions is estimated using an EM algorithm. The approach estimates the regression model,… (More)

- Saahil Shenoy, Dimitry M. Gorinevsky
- IEEE Journal of Selected Topics in Signal…
- 2016

This paper develops a novel approach to computation of the probability integrals encountered in derivative pricing using stochastic models estimated from historical data. First, nonparametric probability distribution models are built directly from the data as a solution of a convex optimization problem scalable to very big datasets. Second, these models are… (More)

Cloud computing applications must be allocated sufficient resources to comply with Service Level Agreements (SLAs). This paper considers data-driven probabilistic modeling of application resource demand for resource allocation. The modeling method is focused on peak demand and SLA violations and relies on a branch of statistics known as extreme value theory… (More)

We have about 42,365 training examples. the come from over 38,000 users over almost 3 months from August-October 2011. In addition, we are given a separate list of about 870 Xbox 360 related SKU’s and some meta information ( including the product description, price history, etc.,) about these items. This list has multiple duplications, and after culling… (More)

- Saahil Shenoy, Dimitry M. Gorinevsky
- 2015 IEEE First International Conference on Big…
- 2015

This paper presents an efficient computational methodology for longitudinal and cross-sectional analysis of extreme event statistics in large data sets. The analyzed data are available across multiple time periods and multiple individuals in a population. Some of the periods and individuals might have no extreme events and some might have much data. The… (More)

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