Abstract
Performance estimation aims at estimating the loss that a predictive model will incur on unseen data. This process is a fundamental stage in any machine learning project. In this paper we study the application of these methods to time series forecasting tasks. For independent and identically distributed data the most common approach is cross-validation. However, the dependency among observations in time series raises some caveats about the most appropriate way to estimate performance in this type of data. Currently, there is no consensual approach. We contribute to the literature by presenting an extensive empirical study which compares different performance estimation methods for time series forecasting tasks. These methods include variants of cross-validation, out-of-sample (holdout), and prequential approaches. Two case studies are analysed: One with 174 real-world time series and another with three synthetic time series. Results show noticeable differences in the performance estimation methods in the two scenarios. In particular, empirical experiments suggest that blocked cross-validation can be applied to stationary time series. However, when the time series are non-stationary, the most accurate estimates are produced by out-of-sample methods, particularly the holdout approach repeated in multiple testing periods.

Abstract

Abstract
Combining adaptive optics and interferometric observations results in a considerable contrast gain compared to single-telescope, extreme AO systems. Taking advantage of this, the ExoGRAVITY project is a survey of known young giant exoplanets located in the range of 0.1"to 2"from their stars. The observations provide astrometric data of unprecedented accuracy, being crucial for refining the orbital parameters of planets and illuminating their dynamical histories. Furthermore, GRAVITY will measure non-Keplerian perturbations due to planet-planet interactions in multi-planet systems and measure dynamical masses. Over time, repetitive observations of the exoplanets at medium resolution (R = 500) will provide a catalogue of K-band spectra of unprecedented quality, for a number of exoplanets. The K-band has the unique properties that it contains many molecular signatures (CO, H2O, CH4, CO2). This allows constraining precisely surface gravity, metallicity, and temperature, if used in conjunction with self-consistent models like Exo-REM. Further, we will use the parameter-retrieval algorithm petitRADTRANS to constrain the C/O ratio of the planets. Ultimately, we plan to produce the first C/O survey of exoplanets, kick-starting the difficult process of linking planetary formation with measured atomic abundances. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

Abstract

Abstract
This article presents an original predictive strategy, based on a new mid-term forecasting model, to be used for trading physical electricity futures. The forecasting model is used to predict the average spot price, which is used to estimate the Risk Premium corresponding to electricity futures trade operations with a physical delivery. A feed-forward neural network trained with the extreme learning machine algorithm is used as the initial implementation of the forecasting model. The predictive strategy and the forecasting model only need information available from electricity derivatives and spot markets at the time of negotiation. In this paper, the predictive trading strategy has been applied successfully to the Iberian Electricity Market (MIBEL). The forecasting model was applied for the six types of maturities available for monthly futures in the MIBEL, from 1 to 6 months ahead. The forecasting model was trained with MIBEL price data corresponding to 44 months and the performances of the forecasting model and of the predictive strategy were tested with data corresponding to a further 12 months. Furthermore, a simpler forecasting model and three benchmark trading strategies are also presented and evaluated using the Risk Premium in the testing period, for comparative purposes. The results prove the advantages of the predictive strategy, even using the simpler forecasting model, which showed improvements over the conventional benchmark trading strategy, evincing an interesting hedging potential for electricity futures trading.

Abstract