In this paper we survey the most recent advances in supervised machine learning and highdimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to penalized regressions and ensemble of models. The...

We consider a joint pricing and inventory control problem in which the customer's response to selling price and the demand distribution are not known a priori. Unsatisfied demand is lost and unobserved, and the only available information for decision-making is the observed sales data (a.k.a....

We study an ambiguity-averse agent with uncertainty about income dynamics who chooses what aspects of the income process to learn about. The agent chooses to learn most about income dynamics at the very lowest frequencies, which have the greatest effect on utility. Deviations of consumption from...

We study an ambiguity-averse agent with uncertainty about income dynamics who chooses what aspects of the income process to learn about. The agent chooses to learn most about income dynamics at the very lowest frequencies, which have the greatest effect on utility. Deviations of consumption from...

We consider a firm (e.g., retailer) selling a single nonperishable product over a finite-period planning horizon. Demand in each period is stochastic and price-dependent, and unsatisfied demands are backlogged. At the beginning of each period, the firm determines its selling price and inventory...

We study a newsvendor problem with unknown demand distribution in a nonstationary demand environment over a multi-period time horizon. The demand in each period consists of a time-varying demand level and an additive random shock. Neither the demand level nor the random shock is separately...