227,310 research outputs found
A comparison of univariate methods for forecasting electricity demand up to a day ahead
This empirical paper compares the accuracy of six univariate methods for short-term electricity demand forecasting for lead times up to a day ahead. The very short lead times are of particular interest as univariate methods are often replaced by multivariate methods for prediction beyond about six hours ahead. The methods considered include the recently proposed exponential smoothing method for double seasonality and a new method based on principal component analysis (PCA). The methods are compared using a time series of hourly demand for Rio de Janeiro and a series of half-hourly demand for England and Wales. The PCA method performed well, but, overall, the best results were achieved with the exponential smoothing method, leading us to conclude that simpler and more robust methods, which require little domain knowledge, can outperform more complex alternatives
Search for high-amplitude Delta Scuti and RR Lyrae stars in Sloan Digital Sky Survey Stripe 82 using principal component analysis
We propose a robust principal component analysis (PCA) framework for the
exploitation of multi-band photometric measurements in large surveys. Period
search results are improved using the time series of the first principal
component due to its optimized signal-to-noise ratio.The presence of correlated
excess variations in the multivariate time series enables the detection of
weaker variability. Furthermore, the direction of the largest variance differs
for certain types of variable stars. This can be used as an efficient attribute
for classification. The application of the method to a subsample of Sloan
Digital Sky Survey Stripe 82 data yielded 132 high-amplitude Delta Scuti
variables. We found also 129 new RR Lyrae variables, complementary to the
catalogue of Sesar et al., 2010, extending the halo area mapped by Stripe 82 RR
Lyrae stars towards the Galactic bulge. The sample comprises also 25
multiperiodic or Blazhko RR Lyrae stars.Comment: 23 pages, 17 figure
Revealing evolutionary constraints on proteins through sequence analysis
Statistical analysis of alignments of large numbers of protein sequences has
revealed "sectors" of collectively coevolving amino acids in several protein
families. Here, we show that selection acting on any functional property of a
protein, represented by an additive trait, can give rise to such a sector. As
an illustration of a selected trait, we consider the elastic energy of an
important conformational change within an elastic network model, and we show
that selection acting on this energy leads to correlations among residues. For
this concrete example and more generally, we demonstrate that the main
signature of functional sectors lies in the small-eigenvalue modes of the
covariance matrix of the selected sequences. However, secondary signatures of
these functional sectors also exist in the extensively-studied large-eigenvalue
modes. Our simple, general model leads us to propose a principled method to
identify functional sectors, along with the magnitudes of mutational effects,
from sequence data. We further demonstrate the robustness of these functional
sectors to various forms of selection, and the robustness of our approach to
the identification of multiple selected traits.Comment: 37 pages, 28 figure
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