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The Analysis of Time Series: An Introduction
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TLDR
In this paper, simple descriptive techniques for time series estimation in the time domain forecasting stationary processes in the frequency domain spectral analysis bivariate processes linear systems state-space models and the Kalman filter non-linear models multivariate time series modelling some other topics.Abstract:
Simple descriptive techniques probability models for time series estimation in the time domain forecasting stationary processes in the frequency domain spectral analysis bivariate processes linear systems state-space models and the Kalman filter non-linear models multivariate time series modelling some other topics.read more
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Procedures for numerical analysis of circadian rhythms.
TL;DR: Various procedures used in the analysis of circadian rhythms at the populational, organismal, cellular and molecular levels are reviewed.
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The TV-tree: an index structure for high-dimensional data
TL;DR: A file structure to index high-dimensionality data, which are typically points in some feature space, and the design of the tree structure and the associated algorithms that handle such “varying length” feature vectors are presented.
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Characteristic-Based Clustering for Time Series Data
TL;DR: This paper proposes a method for clustering of time series based on their structural characteristics, which reduces the dimensionality of the time series and is much less sensitive to missing or noisy data.
Proceedings ArticleDOI
A framework for classifying denial of service attacks
TL;DR: In this article, the authors introduce a framework for classifying DoS attacks based on header content, and novel techniques such as transient ramp-up behavior and spectral analysis, which can be packaged as an automated tool to aid in rapid response to attacks, and can also be used to estimate the level of DoS activity on the Internet.
Journal ArticleDOI
Fast Direct Methods for Gaussian Processes
TL;DR: In this paper, the authors show that for the most commonly used covariance functions, the matrix $C$ can be hierarchically factored into a product of block low-rank updates of the identity matrix, yielding an $\mathcal {O} (n\,\log^2, n)$ algorithm for inversion.