We suggest a new approach for forecasting energy demand at an intraday resolution. The demand in each intraday period is modeled using semiparametric regression smoothing to account for calendar and weather components. Residual serial dependence is captured by one of two multivariate stationary...

The paper describes the problem of forecasting water temperatures on an hourly basis using previous water and air temperatures as predictors. Both time series are decomposed using functional principal components, leading to low dimensional vector autoregressive modeling. The principal component...

Cycling is attracting renewed attention as a mode of transport in western urban environments, yet the determinants of usage are poorly understood. In this paper we investigate some of these using intraday bicycle volumes collected via induction loops located at ten bike paths in the city of...

The paper proposes a small area estimation approach that combines small area random effects with a smooth, non-parametrically specified trend. By using penalized splines as the representation for the non-parametric trend, it is possible to express the non-parametric small area estimation problem...

In this study, we propose a multivariate stochastic model for Web site visit duration, page views, purchase incidence, and the sale amount for online retailers. The model is constructed by composition from carefully selected distributions and involves copula components. It allows for the strong...

In this research we introduce a new class of multivariate probability models to the marketing literature. Known as "copula models," they have a number of attractive features. First, they permit the combination of any univariate marginal distributions that need not come from the same...