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State Representations From Finite Time Series
Proceedings of the 44th IEEE Conference on Decision and Control, 2006We present two algorithms for construction of a state sequence of a linear time-invariant system from a finite exact trajectory of that system. The first algorithm uses the classical in subspace identification splitting of the data into "past" and "future" and computes a bases for the past-future intersection.
I. Markovsky, J.C. Willems, B. De Moor
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1983
Economic data are inherently noisy. We regard them as (discrete-time) stochastic processes, using the first and second order moments to characterize them. By removing known mean values from data yt, the first moments can be taken to be zero. So we focus on the structure of second order moments.
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Economic data are inherently noisy. We regard them as (discrete-time) stochastic processes, using the first and second order moments to characterize them. By removing known mean values from data yt, the first moments can be taken to be zero. So we focus on the structure of second order moments.
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1987
Basically, one can describe time series either in the time domain or in the frequency domain. Difference equations are used in the former, and frequency spectra or transfer functions are used in the latter to specify the dynamic structure of time series. Both representations are used in this book.
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Basically, one can describe time series either in the time domain or in the frequency domain. Difference equations are used in the former, and frequency spectra or transfer functions are used in the latter to specify the dynamic structure of time series. Both representations are used in this book.
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Continuous Representations of Time-Series Gene Expression Data
Journal of Computational Biology, 2003We present algorithms for time-series gene expression analysis that permit the principled estimation of unobserved time points, clustering, and dataset alignment. Each expression profile is modeled as a cubic spline (piecewise polynomial) that is estimated from the observed data and every time point influences the overall smooth expression curve.
Ziv, Bar-Joseph +4 more
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Pattern frequency representation for time series classification
2016 IEEE 8th International Conference on Intelligent Systems (IS), 2016The paper presents a new method for data transformation. The obtained data format enhances the efficiency of the time series classification. The transformation is realized in four steps, namely: time series segmentation, segment's feature representation, segments' binning and pattern frequency representation.
Sergey Milanov, Olga Georgieva
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Exploiting Representational Diversity for Time Series Classification
2012 11th International Conference on Machine Learning and Applications, 2012More than a decade of research has produced numerous representations and similarity measures to support time series classification and clustering. Yet most of the work in the field is so focused on the representation or similarity measure that it ignores the possibility of improving performance using ensembles of representations or classifiers.
Tim Oates +6 more
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Sparse Representation for Time-Series Classification
2016Abstract : This chapter studies the problem of time-series classification and presents an overview of recent developments in the area of feature extraction and information fusion. In particular, a recently proposed feature extraction algorithm, namely symbolic dynamic filtering (SDF), is reviewed.
Soheil Bahrampour +2 more
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Spectral Representation of Univariate Time Series
2017$$\displaystyle{X_{t}\text{ second order stationary, } E\left ( X_{t}\right ) =0\text{, } \gamma _{X}\left ( k\right ) =\int _{-\pi }^{\pi }e^{ik\lambda }dF_{X}\left ( \lambda \right )}$$ $$\displaystyle\Rightarrow \text{additive decomposition of }X_{t} \text{ into periodic components?}$$
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An analysis of time series representation methods
Proceedings of the 2014 ACM Southeast Regional Conference, 2014Because of high dimensionality, proven data mining and pattern recognition methods are not suitable for processing time series data. As a result, several time series representations capable of achieving significant reduction in dimensionality without losing important features have been developed.
Vineetha Bettaiah, Heggere S. Ranganath
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Stochastic Time‐Series Representation of Wave Data
Journal of Waterway, Port, Coastal, and Ocean Engineering, 1992This paper describes a procedure for generating simulated time sequences of wave height, period, and direction data at specific locations. The technique uses a finite length wave record to compute a matrix of coefficient multipliers, which are used to generate arbitrarily long time sequences of simulated wave data, preserving the primary statistical ...
Norman W. Scheffner, Leon E. Borgman
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