Search results for "Data analysis"
showing 10 items of 383 documents
Missing Data in Space-time: Long Gaps Imputation Based On Functional Data Analysis
2017
High dimensional data with spatio-temporal structures are of great interest in many elds of research, but their exhibited complexity leads to practical issues when formulating statistical models. Functional data analysis through smoothing methods is a proper framework for incorporating space-time structures: extending the basic methodology to the multivariate spatio-temporal setting, we refer to Generalized Additive Models for estimating functional data taking the spatial and temporal dependences into account, and to Functional Principal Component Analysis as a classical dimension reduction technique to cope with the high dimensionality and with the number of estimated eects. Since spatial …
Detecting clusters in spatially correlated waveforms
2017
Seismic networks often record signals characterized by similar shapes that provide important information according to their geographic positions. We propose an approach to identify homogeneous clusters of seismic waves, combining analysis of waveforms with metadata and spectrogram information. In waveforms clustering, cross-correlation measures between signals may presents some limitations, so we refer to more recent contributes relating data-depth based clustering analysis. The mechanism for alignment is also an important topic of the analysis: warping (or aligning) procedures identify nuisance effects in phase variation, that, if ignored, may result in a possible loss of information and t…
An Examination of Tourist Arrivals Dynamics Using Short-Term Time Series Data: A Space—Time Cluster Approach
2013
The purpose of this study is to examine the development of Italian tourist areas ( circoscrizioni turistiche) through a cluster analysis of short time series. The technique is an adaptation of the functional data analysis approach developed by Abraham et al (2003), which combines spline interpolation with k-means clustering. The findings indicate the presence of two patterns (increasing and stable) averagely characterizing groups of territories. Moreover, tests of spatial contiguity suggest the presence of ‘space–time clusters’; that is, areas in the same ‘time cluster’ are also spatially contiguous. These findings appear to be more robust in particular for those series characterized by an…
The ALHAMBRA survey: evolution of galaxy clustering since z∼1
2014
We study the clustering of galaxies as function of luminosity and redshift in the range $0.35 < z < 1.25$ using data from the Advanced Large Homogeneous Area Medium Band Redshift Astronomical (ALHAMBRA) survey. The ALHAMBRA data used in this work cover $2.38 \mathrm{deg}^2$ in 7 independent fields, after applying a detailed angular selection mask, with accurate photometric redshifts, $��_z \lesssim 0.014 (1+z)$, down to $I_{\rm AB} < 24$. Given the depth of the survey, we select samples in $B$-band luminosity down to $L^{\rm th} \simeq 0.16 L^{*}$ at $z = 0.9$. We measure the real-space clustering using the projected correlation function, accounting for photometric redshifts uncert…
THE ALHAMBRA SURVEY: EVOLUTION OF GALAXY SPECTRAL SEGREGATION
2016
arXiv:1601.03668v1
Measuring galaxy segregation with the mark connection function
2010
(abridged) The clustering properties of galaxies belonging to different luminosity ranges or having different morphological types are different. These characteristics or `marks' permit to understand the galaxy catalogs that carry all this information as realizations of marked point processes. Many attempts have been presented to quantify the dependence of the clustering of galaxies on their inner properties. The present paper summarizes methods on spatial marked statistics used in cosmology to disentangle luminosity, colour or morphological segregation and introduces a new one in this context, the mark connection function. The methods used here are the partial correlation functions, includi…
The Tucker tensor decomposition for data analysis: capabilities and advantages
2022
Tensors are powerful multi-dimensional mathematical objects, that easily embed various data models such as relational, graph, time series, etc. Furthermore, tensor decomposition operators are of great utility to reveal hidden patterns and complex relationships in data. In this article, we propose to study the analytical capabilities of the Tucker decomposition, as well as the differences brought by its major algorithms. We demonstrate these differences through practical examples on several datasets having a ground truth. It is a preliminary work to add the Tucker decomposition to the Tensor Data Model, a model aiming to make tensors data-centric, and to optimize operators in order to enable…
Eksperimenti ar topoloģisko datu analīzi
2022
Maģistra darba mērķis ir iepazīstināt ar topoloģisko datu analīzi, kas ir pieeja datu kopu analīzei, izmantojot topoloģijas, kā matemātikas novirziena, metodes. Šī inovatīvā datu analīzes metode pasaulē pēdējos gados strauji attīstās un ar vien plašāk tiek pielietota, lai iegūtu informāciju no sarežģītiem, liela apjoma, daudzdimensionāliem datiem. Pašreiz nekur nav atrodams topoloģiskās datu analīzes apraksts un pielietojamība, latviešu valodā. Darbā tiek apskatīti divi dažādi uz topoloģiskās datu analīzes balstīti algoritmi - Mapper un ToMATo, kuru veiksmīgā izmantošanā noteicošais ir pareizu parametru izvēle. Darbā tiek pētītas un piedāvātas šo algoritmu parametru optimizācijas metodes un…
Estimation of wind velocity over a complex terrain using the Generalized Mapping Regressor
2010
Abstract Wind energy evaluation is an important goal in the conversion of energy systems to more environmentally friendly solutions. In this paper, we present a novel approach to wind speed spatial estimation on the isle of Sicily (Italy): an incremental self-organizing neural network (Generalized Mapping Regressor – GMR) is coupled with exploratory data analysis techniques in order to obtain a map of the spatial distribution of the average wind speed over the entire region. First, the topographic surface of the island was modelled using two different neural techniques and by exploiting the information extracted from a digital elevation model of the region. Then, GMR was used for automatic …
XENON1T Dark Matter Data Analysis: Signal Reconstruction, Calibration and Event Selection
2019
The XENON1T experiment at the Laboratori Nazionali del Gran Sasso is the most sensitive direct detection experiment for dark matter in the form of weakly interacting particles (WIMPs) with masses above $6\,$GeV/$c^2$ scattering off nuclei. The detector employs a dual-phase time projection chamber with 2.0 metric tons of liquid xenon in the target. A one metric $\mathrm{ton}\times\mathrm{year}$ exposure of science data was collected between October 2016 and February 2018. This article reports on the performance of the detector during this period and describes details of the data analysis that led to the most stringent exclusion limits on various WIMP-nucleon interaction models to date. In pa…