Year-ahead forecasting of electricity prices is an important issue in the current context of electricity markets. Nevertheless, only one-day-ahead forecasting is commonly tackled up in previous published works. Moreover, methodology developed for the short-term does not work properly for long-term forecasting.In this paper we provide a seasonal extension of the Non-Stationary Dynamic Factor Analysis, to deal with the interesting problem (both from the economic and engineering point of view) of long term forecasting of electricity prices. Seasonal Dynamic Factor Analysis (SeaDFA) allows to deal with dimensionality reduction in vectors of time series, in such a way that extracts common and specific components. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal one, by means of common factors following a multiplicative seasonal VARIMA(p,d,q)×(P,D,Q) s model. Besides, a bootstrap procedure is proposed to be able to make inference on all the parameters involved in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing to enhance the coverage of forecast confidence intervals. Concerning the innovative and challenging application provided, bootstrap procedure developed allows to calculate not only point forecasts but also forecasting intervals for electricity prices.
Keywords:Conditional heteroskedasticity Dynamic factor analysis Iberian market Long run Non-stationary Short runThe liberalization of electricity markets more than ten years ago in the vast majority of developed countries has introduced the need of modelling and forecasting electricity prices and volatilities, both in the short and long term. Thus, there is a need of providing methodology that is able to deal with the most important features of electricity price series, which are well known for presenting not only structure in conditional mean but also time-varying conditional variances. In this work we propose a new model, which allows to extract conditionally heteroskedastic common factors from the vector of electricity prices. These common factors are jointly estimated as well as their relationship with the original vector of series, and the dynamics affecting both their conditional mean and variance. The estimation of the model is carried out under the state-space formulation. The new model proposed is applied to extract seasonal common dynamic factors as well as common volatility factors for electricity prices and the estimation results are used to forecast electricity prices and their volatilities in the Spanish zone of the Iberian Market. Several simplified/alternative models are also considered as benchmarks to ¡Ilústrate that the proposed approach is superior to all of them in terms of explanatory and predictive power.
Nodal peripheral T-cell lymphoma (nodal PTCL) has an unfavorable prognosis, and specific pathogenic alterations have not been fully identified. The biological and clinical relevance of the expression of CD30/T-cell receptor (TCR) genes is a topic under active investigation. One-hundred and ninety-three consecutive nodal PTCLs (89 angioimmunoblastic T-cell lymphomas (AITL) and 104 PTCL-unspecified (PTCL-not otherwise specified (NOS)) cases) were analyzed for the immunohistochemical expression of 19 molecules, involving TCR/CD30 pathways and the associations with standard prognostic indices. Mutually exclusive expression was found between CD3 and TCR-beta F1 with CD30 expression. Taking all PTCL cases together, logistic regression identified a biological score (BS) including TCR molecules (TCR-beta F1 and EZRIN) that separates two subgroups of patients with a median survival of 34.57 and 5.20 months (P<0.001). Multivariate analysis identified BS and the prognostic index for PTCL (PIT) score as independent prognostic factors. This BS maintained its significance in multivariate analysis only for the PTCL-NOS subgroup of tumors. In AITL cases, only a high level of ki67 expression was related to prognosis. A BS including molecules involved in the TCR signaling pathway proved to be an independent prognostic factor of poor outcome in a multivariate analysis, specifically in PTCL-NOS patients. Nevertheless, validation in an independent series of homogeneously treated PTCL patients is required to confirm these data.
The Almendares River is central to recreational and other activities in Havana, Cuba. However, monitoring indicated significant heavy metal contamination in river sediments, especially below Calle 100, the largest landfill in Havana. This work extended previous sediment studies by determining complementary Cu, Pb, Ni, Cr, Cd, and Zn levels in indigenous water hyacinths (Eichhornia crassipes; EC) above and below the landfill. Pb, Cu, and Zn were significantly elevated in EC roots below the landfill and also correlated with sediment data (p < 0.05), implying elevated levels likely result from landfill activity and might be useful biomonitors as river remediation proceeds.
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