We present exact analytic results for a new version of the Minority Game (MG) in which strategy performance is recorded over a finite time horizon. The dynamics of this Time Horizon Minority Game (THMG) exhibit many distinct features from the MG and depend strongly on whether the participants are fed real, or random, history strings. The THMG equations are equivalent to a Markov Chain, and yiel...

We are exploring algorithms to predict the aggregate power output of many photovoltaic systems in a single geographic region on a 3-hour time horizon at 5-minute steps (a 36-step forecast) based only on observed system power. The goal is to correctly identify upcoming “ramp events,” or large positive or negative deviations from a long-term trend over a short time period (Sevlian and Rajagopal, ...

Time-varying asset returns lead highly risk-averse investors to choose market-timing exposures that increase in their horizon, agreement with the common advice reduce risk age, but contrast theoretical work prescribes constant portfolio weights. In a market where an investor absolute aversion and finite horizon trades temporary fluctuations, we find asymptotically optimal investment strategies ...

We study a classical Bayesian statistics problem of sequentially testing the sign of the drift of an arithmetic Brownian motion with the ‘0 − 1’ loss function and a constant cost of observation per unit of time for general prior distributions. The statistical problem is reformulated as an optimal stopping problem with the current conditional probability that the drift is non-negative as the und...

This paper presents a model to solve the short-term scheduling problem of a hydropower plant in a deregulated system, based on dynamic programming techniques. The objective of this model is to maximize the revenue obtained by selling energy in a competitive electricity market. The time horizon of the model is divided into hourly periods and ranges from one day to one week. The proposed model de...

Optimal control theory and machine learning techniques are combined to formulate and solve in closed form an optimal control formulation of online learning from supervised examples with regularization of the updates. The connections with the classical linear quadratic gaussian (LQG) optimal control problem, of which the proposed learning paradigm is a nontrivial variation as it involves random ...

We rephrase the derivation of black hole radiation so as to take into account, at the level of transition amplitudes, the change of the geometry induced by the emission process. This enlarged description reveals that the dynamical variables which govern the emission are the horizon area and its conjugate time variable. Their conjugation is established through the boundary term at the horizon wh...

This paper considers solving optimization problem for linear discrete time systems such that closed-loop discrete-time system is positive (i.e., all of its state variables have non-negative values) and also finite-time stable. For this purpose, by considering a quadratic cost function, an optimal controller is designed such that in addition to minimizing the cost function, the positivity proper...

This paper assesses the performance of a number of long-term interest rate forecast approaches, namely time series models, structural economic models, expert forecasts and combinations thereof. The predictive performance of these approaches is compared using out of sample forecast errors, where a random walk forecast acts as benchmark. It is found that for five major OECD countries, namely Unit...