Ricerc@Sapienza

In this paper we show the importance of the head pose estimation in the task of trajectory forecasting. This cue, when produced by an oracle and injected in a novel socially-based energy minimization approach, allows to get state-of-the-art performances on four different forecasting benchmarks, without relying on additional information such as expected destination and desired speed, which are supposed to be know beforehand for most of the current forecasting techniques.

Recent approaches on trajectory forecasting use tracklets to predict the future positions of pedestrians exploiting Long Short Term Memory (LSTM) architectures. This paper shows that adding vislets, that is, short sequences of head pose estimations, allows to increase significantly the trajectory forecasting performance. We then propose to use vislets in a novel framework called MX-LSTM, capturing the interplay between tracklets and vislets thanks to a joint unconstrained optimization of full covariance matrices during the LSTM backpropagation.

In this paper, we consider the dynamic features of house price in metropolises that are characterised by a high degree of internationalisation. Using a generalised smooth transition (GSTAR) model we show that the dynamic symmetry in house price cycles is strongly rejected for the housing markets considered in this paper. Further, we conduct an out-of-sample forecast comparison of the GSTAR with a linear AR model for the metropolises under consideration. We find that the use of nonlinear models to forecast house prices, in most cases, generate improvements in forecast performance.

High penetration level of intermittent and variable renewable electricity generation introduces signicant challenges
to energy management of modern smart grids. Solar photovoltaics and wind energy have uncertain and non-dispatchable
output which leads to concerns regarding the technical and economic feasibility of a reliable integration of large amounts of
variable generation into electric grids. In this scenario, accurate forecasting of renewable generation outputs is of paramount

Forecasting the energy production of photovoltaic plants is today an essential tool for asset owners because it has direct economic implications on the net operating income of the plants whose generated energy is sold in competitive electricity markets. In this paper, we propose an innovative distributed decentralized prediction technique for the forecasting of power generated by several PV plants.