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.

An analysis of expected delay reduction by replacing a congested at-grade intersection with an interchange.

Texas Department of Transportation, Austin

Automation of an ion chromatograph for precipitation analysis with computerized data reduction /

U.S. Geological Survey, Water Resources Division"--P. [2] of cover.

Computer program for the reduction and preliminary analysis of runoff data.

Mode of access: Internet.

Data analysis methods in optometry Part 8: Principal components analysis (PCA) and factor analysis (FA)

PCA/FA is a method of analyzing complex data sets in which there are no clearly defined X or Y variables. It has multiple uses including the study of the pattern of variation between individual entities such as patients with particular disorders and the detailed study of descriptive variables. In most applications, variables are related to a smaller number of ‘factors’ or PCs that account for the maximum variance in the data and hence, may explain important trends among the variables. An increasingly important application of the method is in the ‘validation’ of questionnaires that attempt to relate subjective aspects of a patients experience with more objective measures of vision.

Kinetic study of the pyrolysis of miscanthus and its acid hydrolysis residue by thermogravimetric analysis

The kinetic parameters of the pyrolysis of miscanthus and its acid hydrolysis residue (AHR) were determined using thermogravimetric analysis (TGA). The AHR was produced at the University of Limerick by treating miscanthus with 5 wt.% sulphuric acid at 175 °C as representative of a lignocellulosic acid hydrolysis product. For the TGA experiments, 3 to 6 g of sample, milled and sieved to a particle size below 250 μm, were placed in the TGA ceramic crucible. The experiments were carried out under non-isothermal conditions heating the samples from 50 to 900 °C at heating rates of 2.5, 5, 10, 17 and 25 °C/min. The activation energy (EA) of the decomposition process was determined from the TGA data by differential analysis (Friedman) and three isoconversional methods of integral analysis (Kissinger–Akahira–Sunose, Ozawa–Flynn–Wall, Vyazovkin). The activation energy ranged from 129 to 156 kJ/mol for miscanthus and from 200 to 376 kJ/mol for AHR increasing with increasing conversion. The reaction model was selected using the non-linear least squares method and the pre-exponential factor was calculated from the Arrhenius approximation. The results showed that the best fitting reaction model was the third order reaction for both feedstocks. The pre-exponential factor was in the range of 5.6 × 1010 to 3.9 × 10+ 13 min− 1 for miscanthus and 2.1 × 1016 to 7.7 × 1025 min− 1 for AHR.

Data Reduction in Intrusion Alert Correlation

Network intrusion detection sensors are usually built around low level models of network traffic. This means that their output is of a similarly low level and as a consequence, is difficult to analyze. Intrusion alert correlation is the task of automating some of this analysis by grouping related alerts together. Attack graphs provide an intuitive model for such analysis. Unfortunately alert flooding attacks can still cause a loss of service on sensors, and when performing attack graph correlation, there can be a large number of extraneous alerts included in the output graph. This obscures the fine structure of genuine attacks and makes them more difficult for human operators to discern. This paper explores modified correlation algorithms which attempt to minimize the impact of this attack.

DEVELOPMENT OF AN OFF-LINE RAINFLOW COUNTING ALGORITHM WITH TEMPORAL PARAMETERS AND DATA REDUCTION

Rainflow counting methods convert a complex load time history into a set of load reversals for use in fatigue damage modeling. Rainflow counting methods were originally developed to assess fatigue damage associated with mechanical cycling where creep of the material under load was not considered to be a significant contributor to failure. However, creep is a significant factor in some cyclic loading cases such as solder interconnects under temperature cycling. In this case, fatigue life models require the dwell time to account for stress relaxation and creep. This study develops a new version of the multi-parameter rainflow counting algorithm that provides a range-based dwell time estimation for use with time-dependent fatigue damage models. To show the applicability, the method is used to calculate the life of solder joints under a complex thermal cycling regime and is verified by experimental testing. An additional algorithm is developed in this study to provide data reduction in the results of the rainflow counting. This algorithm uses a damage model and a statistical test to determine which of the resultant cycles are statistically insignificant to a given confidence level. This makes the resulting data file to be smaller, and for a simplified load history to be reconstructed.

Factor analysis applied to genome prediction for high-dimensional phenotypes in pigs.

The aim of the present study was to propose and evaluate the use of factor analysis (FA) in obtaining latent variables (factors) that represent a set of pig traits simultaneously, for use in genome-wide selection (GWS) studies. We used crosses between outbred F2 populations of Brazilian Piau X commercial pigs. Data were obtained on 345 F2 pigs, genotyped for 237 SNPs, with 41 traits. FA allowed us to obtain four biologically interpretable factors: ?weight?, ?fat?, ?loin?, and ?performance?. These factors were used as dependent variables in multiple regression models of genomic selection (Bayes A, Bayes B, RR-BLUP, and Bayesian LASSO). The use of FA is presented as an interesting alternative to select individuals for multiple variables simultaneously in GWS studies; accuracy measurements of the factors were similar to those obtained when the original traits were considered individually. The similarities between the top 10% of individuals selected by the factor, and those selected by the individual traits, were also satisfactory. Moreover, the estimated markers effects for the traits were similar to those found for the relevant factor.

Long-term social recognition memory in adult male rats: factor analysis of the social and non-social behaviors

A modified version of the intruder-resident paradigm was used to investigate if social recognition memory lasts at least 24 h. One hundred and forty-six adult male Wistar rats were used. Independent groups of rats were exposed to an intruder for 0.083, 0.5, 2, 24, or 168 h and tested 24 h after the first encounter with the familiar or a different conspecific. Factor analysis was employed to identify associations between behaviors and treatments. Resident rats exhibited a 24-h social recognition memory, as indicated by a 3- to 5-fold decrease in social behaviors in the second encounter with the same conspecific compared to those observed for a different conspecific, when the duration of the first encounter was 2 h or longer. It was possible to distinguish between two different categories of social behaviors and their expression depended on the duration of the first encounter. Sniffing the anogenital area (49.9% of the social behaviors), sniffing the body (17.9%), sniffing the head (3%), and following the conspecific (3.1%), exhibited mostly by resident rats, characterized social investigation and revealed long-term social recognition memory. However, dominance (23.8%) and mild aggression (2.3%), exhibited by both resident and intruders, characterized social agonistic behaviors and were not affected by memory. Differently, sniffing the environment (76.8% of the non-social behaviors) and rearing (14.3%), both exhibited mostly by adult intruder rats, characterized non-social behaviors. Together, these results show that social recognition memory in rats may last at least 24 h after a 2-h or longer exposure to the conspecific.

Drag reduction by polyethylene glycol in the tail arterial bed of normotensive and hypertensive rats

This study was designed to evaluate the effect of drag reducer polymers (DRP) on arteries from normotensive (Wistar) and spontaneously hypertensive rats (SHR). Polyethylene glycol (PEG 4000 at 5000 ppm) was perfused in the tail arterial bed with (E+) and without endothelium (E-) from male, adult Wistar (N = 14) and SHR (N = 13) animals under basal conditions (constant flow at 2.5 mL/min). In these preparations, flow-pressure curves (1.5 to 10 mL/min) were constructed before and 1 h after PEG 4000 perfusion. Afterwards, the tail arterial bed was fixed and the internal diameters of the arteries were then measured by microscopy and drag reduction was assessed based on the values of wall shear stress (WSS) by computational simulation. In Wistar and SHR groups, perfusion of PEG 4000 significantly reduced pulsatile pressure (Wistar/E+: 17.5 ± 2.8; SHR/E+: 16.3 ± 2.7%), WSS (Wistar/E+: 36; SHR/E+: 40%) and the flow-pressure response. The E- reduced the effects of PEG 4000 on arteries from both groups, suggesting that endothelial damage decreased the effect of PEG 4000 as a DRP. Moreover, the effects of PEG 4000 were more pronounced in the tail arterial bed from SHR compared to Wistar rats. In conclusion, these data demonstrated for the first time that PEG 4000 was more effective in reducing the pressure-flow response as well as WSS in the tail arterial bed of hypertensive than of normotensive rats and these effects were amplified by, but not dependent on, endothelial integrity. Thus, these results show an additional mechanism of action of this polymer besides its mechanical effect through the release and/or bioavailability of endothelial factors.