Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting

In this work, we propose the Seasonal Dynamic Factor Analysis (SeaDFA), an extension of Nonstationary Dynamic Factor Analysis, through which one can deal with dimensionality reduction in vectors of time series in such a way that both common and specific components are extracted. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal ones, by means of the common factors following a multiplicative seasonal VARIMA(p, d, q) × (P, D, Q)s model. Additionally, a bootstrap procedure that does not need a backward representation of the model is proposed to be able to make inference for all the parameters in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing enhanced coverage of forecasting intervals. A challenging application is provided. The new proposed model and a bootstrap scheme are applied to an innovative subject in electricity markets: the computation of long-term point forecasts and prediction intervals of electricity prices. Several appendices with technical details, an illustrative example, and an additional table are available online as Supplementary Materials.

Factor analysis for metal grade exploration at Pallancata Vein in Peru

Se investiga la distribución espacial de contenidos metálicos analizados sobre testigos de sondeos obtenidos en las campañas de exploración de la Veta Pallancata. Se aplica el análisis factorial a dicha distribución y a los cocientes de los valores metálicos, discriminando los que están correlacionados con la mineralización argentífera y que sirven como guías de exploración para hallar zonas de potenciales reservas por sus gradientes de variación.Abstract:The metal distribution in a vein may show the paths of hydrothermal fluid flow at the time of mineralization. Such information may assist for in-fill drilling. The Pallancata Vein has been intersected by 52 drill holes, whose cores were sampled and analysed, and the results plotted to examine the mineralisation trends. The spatial distribution of the ore is observed from the logAg/logPb ratio distribution. Au is in this case closely related to Ag (electrum and uytenbogaardtite, Ag3AuS2 ). The Au grade shows the same spatial distribution as the Ag grade. The logAg/logPb ratio distribution also suggests possible ore to be expected at deeper locations. Shallow supergene Ag enrichment was also observed.

Urban built environment analysis: evidence from a mobility survey in Madrid

Applications involving travel behavior from the perspective of land use are dating from the 1990s. Usually, four important components are distinguished: density, diversity and design (3D?s of Cervero and Kockelman) and accessibility (introduced by Geurs and van Wee). But there is not a general agreement on how to measure each of those 4 components. Density is used to be measured as population and employment densities, but others authors separate population density between residential and building densities. A lot of measures have been developed to estimate diversity: among others, a dissimilarity index to indicate the degree to which different land uses lie within one another?s surrounding, an entropy index to quantify the degree of balance across various land use types or proximities to commercial-retail uses. Design has been characterized by site design, and dwelling and street characteristics. Lastly, accessibility has become a frequently used concept, but its meaning on travel behavior field always refers to the ability ?to reach activities or locations by means of a travel mode?, measured as accessibility to jobs, to leisure activities, and others. Furthermore, the previous evidence is mainly based on US data or on north European countries. Therefore, this paper adds some new evidence from a Spanish perspective to the research debate. Through a Madrid smartphone-based survey, factor analysis is used to linearly combine variables into the 3D?s and accessibility dimensions of the built environment. At a first step for future investigations, land use variables will be treated to define accurately the previous 4 components.

A dynamic factor model for mid-term forecasting of wind power generation

The main objective of this paper is the development and application of multivariate time series models for forecasting aggregated wind power production in a country or region. Nowadays, in Spain, Denmark or Germany there is an increasing penetration of this kind of renewable energy, somehow to reduce energy dependence on the exterior, but always linked with the increaseand uncertainty affecting the prices of fossil fuels. The disposal of accurate predictions of wind power generation is a crucial task both for the System Operator as well as for all the agents of the Market. However, the vast majority of works rarely onsider forecasting horizons longer than 48 hours, although they are of interest for the system planning and operation. In this paper we use Dynamic Factor Analysis, adapting and modifying it conveniently, to reach our aim: the computation of accurate forecasts for the aggregated wind power production in a country for a forecasting horizon as long as possible, particularly up to 60 days (2 months). We illustrate this methodology and the results obtained for real data in the leading country in wind power production: Denmark