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Most electricity spot price series exhibit price spikes. These extreme observations may significantly impact the obtained model estimates and hence reduce efficiency of the employed predictive algorithms. For markets with only positive prices, the logarithmic transform is the single most commonly used technique to reduce spike severity and consequently stabilize the variance. However, for datasets with very close to zero (like the Spanish) or negative (like the German) prices the log-transform is not feasible. What reasonable choices do we have then? To address this issue, we evaluate 16 variance stabilizing transformations within a comprehensive forecasting study involving two model classes (regression models, neural networks) and 12 datasets from diverse power markets. We show that the probability integral transform combined with the standard Gaussian distribution yields the best approach, significantly better than many of the considered alternatives. [ABSTRACT FROM AUTHOR]

In this paper we investigate whether considering the fine structure of half-hourly electricity prices, the market closing prices of fundamentals (natural gas, coal and CO2) and the system-wide demand can lead to significantly more accurate short- and mid-term forecasts of APX U.K. baseload prices. We evaluate the predictive accuracy of a number of univariate and multivariate time series models over a three-year out-of-sample forecasting period and compare it against that of a benchmark autoregressive model. We find that in the short-term, up to a few business days ahead, a disaggregated model which independently predicts the intra-day prices and then takes their average to yield baseload price forecasts is the best performer. However, in the mid-term, factor models which explore the correlation structure of intra-day prices lead to significantly (as measured by the Diebold-Mariano test) better baseload price forecasts. At the same time, we observe that the inclusion of fundamental variables—especially natural gas prices (in the short-term) and coal prices (in the mid-term)—provides significant gains. The CO2 prices, on the other hand, generally do not improve the price forecasts at all, at least in the time period considered in this study (April 2009–December 2013). [ABSTRACT FROM PUBLISHER]

A major issue in financial economics is the behavior of asset returns over long horizons. Various estimators of long-range dependence have been proposed. Even though some have known asymptotic properties, it is important to test their accuracy by using simulated series of different lengths. We test R/S analysis, Detrended Fluctuation Analysis and periodogram regression methods on samples drawn from Gaussian white noise. The DFA statistics turns out to be the unanimous winner. Unfortunately, no asymptotic distribution theory has been derived for this statistics so far. We were able, however, to construct empirical (i.e. approximate) confidence intervals for all three methods. The obtained values differ largely from heuristic values proposed by some authors for the R/S statistics and are very close to asymptotic values for the periodogram regression method. [Copyright &y& Elsevier]

The article presents a research conducted by Tomasz Czarnik and Ryszard Gawda regarding the safeness and efficacy of supraclavicular approach method to the patients who are mechanically ventilated. Accordingly, it is found out that central venous catheters are usually used in every hemodynamic monitoring and supervision as well as various therapeutic administrations. Researchers have conducted a study in order to determine the safeness and efficacy of supraclavicular approach method. Furthermore, researchers found that supraclavicular approach can be an effective method in subclavian venous catheterization to mechanically ventilated patients.

Abstract: Introduction: Anatomical proximity of the eye and the intracranial space is a fact but the existence of physiological and pathophysiological relationships between them is elusive. The objective of this study was to explore anatomical and pathophysiological interactions between the eye and the intracranial space and to assess clinical utility of intraocular pressure measurement in estimation of intracranial pressure in patients with brain injuries and to discover how haemodynamic instability could influence these interactions. Controversy surrounds the recent literature concerning this problem and the consensus has not been achieved. Materials and methods: We evaluated the correlation between intracranial pressure and intraocular pressure, intracranial pressure and mean arterial pressure, intraocular pressure and mean arterial pressure in 40 patients with brain injuries initially comatose, admitted to our hospital. All patients required the intracranial pressure monitoring on clinical grounds. Simultaneous recordings of intracranial pressure, intraocular pressure and mean arterial pressure were performed. Results: We calculated both the linear correlation coefficient and the Spearman rank-order correlation coefficient for all three relations. We found significant correlation between intraocular pressure and mean arterial pressure in 63% of the tested population. When the power of the test was increased, by considering only patients with 11 or more observations, this ratio increased to 76%. However, the correlation between intraocular pressure and intracranial pressure, as well as, between intracranial pressure and mean arterial pressure was not significant. Conclusions: There is no anatomical and pathophysiological basis for the statement that intraocular pressure can be used as an indirect estimator of intracranial pressure. [Copyright &y& Elsevier]