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The Analysis of Time Series: An Introduction
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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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Journal ArticleDOI
Quantifying temporal change in biodiversity: challenges and opportunities
Maria Dornelas,Anne E. Magurran,Stephen T. Buckland,Anne Chao,Robin L. Chazdon,Robert K. Colwell,Thomas P. Curtis,Kevin J. Gaston,Nicolas J. Gotelli,Matthew A. Kosnik,Brian J. McGill,Jenny L. McCune,Hélène Morlon,Peter J. Mumby,Lise Øvreås,A. C. Studeny,A. C. Studeny,Mark Vellend +17 more
TL;DR: This work explores methods of quantifying change in biodiversity at different timescales, noting that autocorrelation can be viewed as a feature that sheds light on the underlying structure of temporal change.
Book ChapterDOI
Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization
TL;DR: Two strategies for population re-initialization are introduced when a change in the environment is detected, one to predict the new location of individuals from the location changes that have occurred in the history and one to perturb the current population with a Gaussian noise whose variance is estimated according to previous changes.
Journal ArticleDOI
Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects
TL;DR: The method can be employed in classifying trajectory data generated by unknown moving objects and assigning them to known types of moving objects, whose movement characteristics have been previously learned and can be successfully applied in automatic transport mode detection.
Journal ArticleDOI
Biomedical signal processing (in four parts). Part 3. The power spectrum and coherence function.
R. E. Challis,R.I. Kitney +1 more
TL;DR: This is the third in a series of four tutorial papers on biomedical signal processing and concerns the estimation of the power spectrum (PS) and coherence function (CF) od biomedical data.