Time-series data mining.
Massive distribution for indexing and mining time series
... representative time series is used as a template ...continuous data stream to detect new occurrences of that same ...A time series at a given sensor functions like a geophysical ...
147
Mining Multivariate Heterogeneous Time Series Models with Computational Intelligence Techniques
... ror, as indicated by the comparison, for example, between the 90% and 10% cases. All of these features are indicative of very robust behavior. This skewed distribution is also an indication of the algorithm’s ...
8
Behaviour of Similarity-Based Neuro-Fuzzy Networks and Evolutionary Algorithms in Time Series Model Mining
... the data concerning type, volume, homogeneity, complexity, preci- sion, the curse of dimensionality, ...multivariate time series was introduced in ...large series with different degrees of ...
7
Mining multivariate time series models with soft-Computing techniques: a coarse-grained parallel computing approach
... d e = (1/card(A c )) P A c (x i − y i ) 2 , which is a normalized distance and therefore, independent of the number of attributes. Consequently, no imputation of miss- ing values to the data set is performed. The ...
12
Discrete Elastic Inner Vector Spaces with Application in Time Series and Sequence Mining
... Discrete time series, Sequence mining, Non-uniform sampling, Elastic inner product, Time ...IME series analysis in metric spaces has attracted much attention over numerous decades and ...
15
Data Mining: Understanding Data and Disease Modeling
... The data characteristics checking algorithm identifies anomalies based on: (i) the number of dependencies selected (0-3), (ii) preferred tolerance, and (iii) the order of attributes (time-series or ...
8
Sequential pattern mining on multimedia data
... 13-dimensional time series. Then, this multivariate time series is transformed into a sequence of ...transforming time series into a sequence of ...
8
Biodiversity and Environment Data Mining
... 4 Data Mining for Biodiversity 4.1 Data Mining Concept Data mining, also known as knowledge discovery from data (KDD), is a set of concepts, methods and tools for the ...
21
Data stream management and mining
... of data management has been defined to handle “data streams” which are infinite sequences of structured records arriving continuously in real ...designed data processing systems called “Data ...
15
Wavelets and Time Series Modeling
... exploratory data analysis, and ...of data of arbitrary size not restricted to powers of 2; both command line and graphical user interfaces with a comprehensive set of plots and visual displays; an object ...
48
Comparative performance of selected variability detection techniques in photometric time series data
... We compare the performance of popular variability detection techniques on various real and simulated photometric data sets. We refer to any value that quantifies ‘how variable’ a given object is as a ‘variability ...
29
Reconstructing X'-deterministic extended Petri nets from experimental time-series data X'
... nets, time-series data, priority relations, control-arcs Résumé Ce travail a pour but de reconstruire des modèles sous forme de réseaux de Petri pour des systèmes biologiques à partir des données ...
20
Visual data mining from visualization to visual information mining
... representing data or subsets of ...the data mining ...data mining. In traditional visualization, the human subject looks at the data from outside, while in a VR environment the ...
16
Visualization techniques for data mining
... of data and information, mainly caused by data warehousing technologies as well as the extensive use of the Internet and its related technologies, has increased the urgent need for the development of ...
16
Nonlinear models for neurophysiological time series
... the data given the model, to compare different choice of hyper- ...legitimate data-driven approach, whereas optimizing for the highest PAC score is statistically more ...unseen data, it naturally ...
145
Boolean Network Identification from Perturbation Time Series Data combining Dynamics Abstraction and Logic Programming
... from time series ...from time series gene expression data [9, ...phosphoproteomic time series ...only data and no prior knowledge network, obtained a significant ...
22
Homogenizing GPS Integrated Water Vapor Time Series: Benchmarking Break Detection Methods on Synthetic Data Sets
... IWV series (candidate minus reference ...Fourier series of order ...the time, the three penalties select the same number of segments K, but in some cases, the results can differ, with some of them ...
21
Pattern extraction for time-series classification
... BMRi_®ó^LJaF8ARlCAaACFYf F8fgIKF BMRi_]y`!Aaj_ î&LJaFYA}FYfEf F8fgIKF BMRi_]y`!AajAaP,L3_î&LJCFYAR³laACAaFYf P,LJaF8HK BEIKF h8NmfEBEA]jCF7H.[r] ...
12
Sequential Quantile Prediction of Time Series
... n→∞ Ln(g) = L ⋆ a.s. Thus, consistent strategies asymptotically achieve the best possible loss for all processes in the class. In the context of prediction with squared loss, Gy¨orfi and Lugosi [11], Nobel [12], Gy¨orfi ...
27
Causal inference with time-series cross-sectional data : with applications to positive political economy
... clustered at the village level are in parentheses. The independent variable is a dummy variable indicating whether a VC came from the village's largest or second-largest [r] ...
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