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[PDF] Top 20 Outlier identification in multivariate time series

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Outlier identification in multivariate time series

Outlier identification in multivariate time series

... When analyzing the prediction results, it was notable to conclude that the kNN model is not suitable for this data set. The predictions were always the "hot water" class for any test observation (corresponding to ... See full document

64

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

Forecasting multivariate time series under present-value-model short- and long-run co-movement restrictions

... forecasting series that are subject to PVM re- strictions, which has a wide application in macroeconomics and ...features in its various ...chosen in a way that is aligned with the …nal ... See full document

41

Identification of inhomogeneities in precipitation time series using sur models and the Ellipse test

Identification of inhomogeneities in precipitation time series using sur models and the Ellipse test

... (i) series considered homogeneous by all tests were classified as ‘reference’; (ii) series for which only one of the six tests rejected the null hypothesis were classified as ‘candidate’; (iii) ... See full document

10

Research article Identification of neutral biochemical network models from time series data

Research article Identification of neutral biochemical network models from time series data

... data. In other words, the data are not informative enough to distinguish between alterna- tive models that fit equally ...well. In the past, such situa- tions have often been "resolved" by setting ... See full document

13

Water monitoring sites discrimination using clustering water variables time series data and main latent factors identification

Water monitoring sites discrimination using clustering water variables time series data and main latent factors identification

... The Ave hydrological basin is located in Northwest Portugal and its main adjacent streams are the rivers Este, Selho, and Vizela. The surface water of River Ave has high pollution levels and the water quality ... See full document

6

Time series forecasting for a call center in a Warsaw holding company

Time series forecasting for a call center in a Warsaw holding company

... study time series were mostly based on the idea of a deterministic world until 1927, when the first major breakthrough in the area of time series forecasting took place essentially ... See full document

73

Missing Data in Time Series: Analysis, Model and Software Application

Missing Data in Time Series: Analysis, Model and Software Application

... However, in most missing data situations, it is rare to have the possibility to directly speaking with the ...the series regards the aggregate consumption of cocaine in the last 20 years and some of ... See full document

80

On reconstruction of time series in climatology

On reconstruction of time series in climatology

... a multivariate linear regression equation ...transformed in some way before the reconstruction (for example, time series of princi- pal components of expansions into empirical orthogonal ... See full document

28

Multifractality signatures in quasars time series – I. 3C 273

Multifractality signatures in quasars time series – I. 3C 273

... multifractality in a time series shows different correlations for different time- scales as well as intermittent behaviour that cannot be captured by a single scaling ...The ... See full document

11

2 Structural Time Series Models

2 Structural Time Series Models

... increase in availability of economic data in recent years and, more impor- tantly, the possibility to construct larger frequency time series, have fostered the use (and development) of ... See full document

30

DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

DETECT: a MATLAB toolbox for event detection and identification in time series, with applications to artifact detection in EEG signals.

... EEG time series. SCADS [27], or Statistical Control of Artifacts in Dense Array Studies, is a method for removing artifacts in EEG before the analysis of event- related potentials ...where ... See full document

13

Identification of catchment functional units by time series of thermal remote sensing images

Identification of catchment functional units by time series of thermal remote sensing images

... on time series of thermal remote sensing images is ...the time series are spatio–temporally ...the time series were extracted via PCA and could be related to physical ecological ... See full document

15

Simulating Price Interactions by Mining Multivariate Financial Time Series

Simulating Price Interactions by Mining Multivariate Financial Time Series

... presented in [Marques and Cavique, ...ations in American Dow Jones (DJI) and 179 times by Ger- man ...assets in the ...tendencies in these long term ... See full document

5

Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry

Multivariate autoregressive modelling of sea level time series from TOPEX/Poseidon satellite altimetry

... level time series from TOPEX/Poseidon satellite al- timetry ...both in time and space. Multivariate analysis methods are useful tools to summarise and extract information from such ... See full document

8

Identification of Linear Periodically Time-Varying Systems Based on Hartley Series

Identification of Linear Periodically Time-Varying Systems Based on Hartley Series

... conditions in the interval (0, !) can be approximated by means of a matrix product, as indicated by ...terms in the series is related to the error level; in this case, more terms mean a better ... See full document

6

Time Series Analysis based on Complex Networks

Time Series Analysis based on Complex Networks

... to multivariate and spatio- temporal ...integrate in a single complex network system several different variables (for example, sea level, temperature and pressure could be represented on different layers ... See full document

138

Data imputation analysis for Cosmic Rays time series

Data imputation analysis for Cosmic Rays time series

... siders that all variables in a dataset have multivariate normal distribution (MVN), using mean and covariance to summarize data. The imputation is carried out ran- domly, so, it failed to represent the ... See full document

16

Principal and Independent Component Analysis in Financial Time Series

Principal and Independent Component Analysis in Financial Time Series

... to multivariate financial time series, particularly the Correlation matrix, the Forecastable Component Analysis, the Mutual Information, the Kullback-Leibler Divergence, the Ap- proximate Entropy, ... See full document

191

Output convergence in Mercosur: multivariate time series evidence

Output convergence in Mercosur: multivariate time series evidence

... 491. FORECASTING ELECTRICITY LOAD DEMAND: ANALYSIS OF THE 2001 RATIONING PERIOD IN BRAZIL - Leonardo Rocha Souza; Lacir Jorge Soares – Julho de 2003 – 27 págs. 492. THE MISSING LINK: USING THE NBER RECESSION ... See full document

41

Bayesian Outlier Detection in Non-Gaussian Autoregressive Time Series

Bayesian Outlier Detection in Non-Gaussian Autoregressive Time Series

... where B(⋅, ⋅) is the beta function, the observations are also marginally negative binomial, that is, X t ∼ F 𝜇 . This model corresponds to the beta-binomial thinning of McKenzie (1986). Table I summarizes some particular ... See full document

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