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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
Citations
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Journal ArticleDOI
Feature extraction of the first difference of EMG time series for EMG pattern recognition
Angkoon Phinyomark,Franck Quaine,Sylvie Charbonnier,Christine Serviere,Franck Tarpin-Bernard,Yann Laurillau +5 more
TL;DR: The utility of a differencing technique to transform surface EMG signals measured during both static and dynamic contractions such that they become more stationary is demonstrated.
Proceedings ArticleDOI
Opportunistic Mobile Sensor Data Collection with SCAR
TL;DR: SCAR, a context aware opportunistic routing protocol which allows efficient routing of sensor data to sinks, through selection of best paths by prediction over movement patterns and current battery level of nodes is devised.
Proceedings ArticleDOI
The TS-tree: efficient time series search and retrieval
TL;DR: The TS-tree (time series tree) is proposed, an index structure for efficient time series retrieval and similarity search that outperforms existing approaches like the R*-tree or the quantized A-tree by exploiting inherent properties of time series quantization and dimensionality reduction.
Journal ArticleDOI
Methods, algorithms and tools in computational proteomics: A practical point of view
TL;DR: A broad overview of a number of computational tools available for data analysis of MS‐based proteomics data is provided and appropriate literature references are given to detailed description of algorithms.
Prototype Selection for Composite Nearest Neighbor Classifiers
TL;DR: Algorithms that combine a small number of component nearest neighbor classifiers, where each of the components stores aSmall number of prototypical instances are introduced, which yield composite classifiers that are more accurate than a nearest neighbors classifier that stores all training instances as prototypes.