The paper investigates the impact of jumps in forecasting co-volatility, accommodating leverage effects. We modify the jump-robust two time scale covariance estimator of Boudt and Zhang (2013) such that the estimated matrix is positive definite. Using this approach we can disentangle the estimates of the integrated co-volatility matrix and jump variations from the quadratic covariation matrix. ...

We present methods employed in COORDINATE, a prototype service that supports collaboration and communication by learning predictive models that provide forecasts of users’ presence and availability. We describe how data is collected about user activity and proximity from multiple devices, in addition to analysis of the content of users’ calendars, the time of day, and day of week. We review app...

Road maintenance is one of the main problems Departments of Transportation face during winter time. Anti-icing, i.e. applying chemicals to the road to prevent ice formation, is often used to keep the roads free of ice. Given the preventive nature of anti-icing, accurate predictions of road ice are needed. Currently, anti-icing decisions are usually based on deterministic weather forecasts. Howe...

this paper presents a probabilistic optimal power flow (popf) algorithm considering different uncertainties in a smart grid. different uncertainties such as variation of nodal load, change in system configuration, measuring errors, forecasting errors, and etc. can be considered in the proposed algorithm. by increasing the penetration of the renewable energies in power systems, it is more essent...

This work compares the performance of machine learning methods (k-nearest-neighbors (kNN) and gradient boosting (GB)) in intra-hour forecasting of global (GHI) and direct normal (DNI) irradiances. The models predict the GHI and DNI and the corresponding prediction intervals. The data used in this work include pyranometer measurements of GHI and DNI and sky images. Point forecasts are evaluated ...

We present methods employed in COORDINATE, a prototype service that supports collaboration and communication by learning predictive models that provide forecasts of users' presence and availability. We describe how data is collected about user activity and proximity from multiple devices, in addition to analysis of the content of users' calendars, the time of day, and day of week. We review app...

Energy price forecasting is a relevant yet hard task in the field of multi-step time series forecasting. In this paper we compare a wellknown and established method, ARMA with exogenous variables with a relatively new technique Gradient Boosting Regression. The method was tested on data from Global Energy Forecasting Competition 2014 with a year long rolling window forecast. The results from th...

Photovoltaic (PV) generation is potentially uncertain. Probabilistic PV forecasting methods have been proposed with prediction intervals (PIs) to evaluate the uncertainty quantitively. However, few studies applied PIs geographically distributed PVs in a specific area. In this study, two-step probabilistic forecast scheme for forecasting. Each step of adopts ensemble based on three different mac...

Low-rank matrix factorizations such as Principal Component Analysis (PCA), Singular Value Decomposition (SVD) and Non-negative Matrix Factorization (NMF) are a large class of methods for pursuing the low-rank approximation of a given data matrix. The conventional factorization models are based on the assumption that the data matrices are contaminated stochastically by some type of noise. Thus t...