high-dimensional time-series data
TSmap3D: Browser Visualization of High Dimensional Time Series Data
... alizing high dimensional time series data in 3D space as a sequence of 3D point ...mapping high dimensional data to 3D from large data sets, and a generic ...
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MPSKM Algorithm to Cluster Uneven Dimensional Time Series Subspace Data
... in data which may occur due to human error or some abnormal events occurred while creating data set[9, ...and high dimensional data ...
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On K-Means Cluster Preservation using Quantization Schemes
... the high-dimensional objects in a dataset represent time-series, ...1-D time series are collected into a T dimensional vector), the proposed quantization retains very ...
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Dynamic Orthogonal Subseries for High-Dimensional and Nonstationary Time Series
... the data and existence of time-invariance of the linear ...nonstationary time series models with evolutionary spectral representation which can be approximated arbitrarily closely by AR models ...
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Two Distributed-State Models For Generating High-Dimensional Time Series
... capture data often contains missing or unusable ...of time due to sensor failure or ...missing data, but this leaves the data unnaturally ...future data from the single missing marker ...
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Inferring the Global Financial Network from High-Dimensional Time-Series of Stock Returns
... maz [2012] investigated volatility spillovers between four asset classes including stock, bond, commodity, and FX using U.S. market data between 1999 and 2010. Diebold and Yilmaz [2014] studied connectedness ...
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Anomaly Detection on Time Series Data
... a high-dimensional space. Since the data can be converted to a a lower dimensional subspace we can find anomalous points by observing the deviation from this ...consider time-domain ...
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Efficient search methods for high dimensional time series
... multiple time-series with the aim of detecting abnormal ...the data change from some normal or baseline ...using data from multiple ...CNV data, and give evidence that this approach is ...
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Semiparametric Ultra-High Dimensional Model Averaging of Nonlinear Dynamic Time Series
... in time series data, the iterative sure independence screening procedure developed in Fan et al (2011) cannot be applied in our ...observed data is randomly permuted to obtain a ...
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Semiparametric Ultra-High Dimensional Model Averaging of Nonlinear Dynamic Time Series
... The data were collected from the Office for National Statistics (ONS) and the Bank of England (BoE) websites and included quarterly observations on CPI and some other economics variables over the period Q1 1997 to ...
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Essays in high dimensional nonlinear time series analysis
... the data lives close to a lower dimensional linear ...the data lies on or near a low-dimensional nonlinear manifold, then linear methods, even though computationally efficient, can not model ...
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Factor modeling for high dimensional time series
... functional data analysis with independent observations, the work on functional time series has been of a more theoretical nature; see ...functional series (Part IV of Ferraty & Vieu ...
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Sparse modelling and estimation for nonstationary time series and high dimensional data
... the data changes significantly, the underlying function may not be piecewise constant itself; on the other hand, the time series model used in Chapter 3 has piecewise constant com- ponents in its ...
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Spectral analysis of high dimensional time series
... the data within certain ...the data can be obtained from the inverse of the spectral density matrix ...the time series is relatively large compared to the length of the time ...
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Essays in High Dimensional Time Series Analysis
... Ever since the introduction of AdaBoost in the 1990’s (Freund and Schapire, 1997), boosting algorithms have been one of the most successful and widely utilized machine learning methods (Friedman et al., 2001). AdaBoost, ...
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Booster in High Dimensional Data Classification
... This paper proposes Q-statistic to evaluate the performance of an FS algorithm with a classifier. This is a hybrid measure of the prediction accuracy of the classifier and the stability of the selected features. Then the ...
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Detection of Outliers in Time Series Data
... synthetic data sets with an average of 46 synthetic ...identified data set and synthetic data ...both data sets are similar ...two data vectors x1 and x2. In our case x1 = identified ...
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On time series data and optimal parameters
... Forecasting using time series (TS) models are often based on linear regression or methods using various smoothing techniques. When estimating the parameters used in smoothing techniques, it has become a ...
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Time series modelling of birth data
... the high fluctuations in the age-specific fertility rates were the error sources causing most available birth forecasts based on these rates to be inaccurate, and suggested the use of the birth order probabilities ...
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Analysis Challenges for High Dimensional Data
... microarray data can be found in the R package plsgenomics with name leukemia which has 3051 genes for 38 leukemia pa- ...this data analysis is to find other genes (3050 in total) whose expressions are ...
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