Results 271 to 280 of about 3,433,600 (309)
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Characterization of time series for analyzing of the evolution of time series clusters
Expert Systems with Applications, 2015We propose a characterization of time series for multivariable temporal databases.For the characterization of time-series we used the level and trend components.The characterization the time-series is adequate for long and short periods of time.Our proposal allows analysis the evolution of the groups and objects.We developed an R-based script for ...
Ana P. Serra, Luis E. Zárate
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2017
In this chapter, we describe three different synthetic datasets that we considered to evaluate the performance of the reviewed recurrent neural network architectures in a controlled environment. The generative models of the synthetic time series are the Mackey–Glass system, NARMA, and multiple superimposed oscillators.Those are benchmark tasks commonly
Bianchi, Filippo Maria +4 more
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In this chapter, we describe three different synthetic datasets that we considered to evaluate the performance of the reviewed recurrent neural network architectures in a controlled environment. The generative models of the synthetic time series are the Mackey–Glass system, NARMA, and multiple superimposed oscillators.Those are benchmark tasks commonly
Bianchi, Filippo Maria +4 more
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Models of time series with time granulation
Knowledge and Information Systems, 2016Albeit simple and easy to interpret, a piecewise representation of time series comes with discontinuities that inevitably lead to substantial representation (approximation) error. In this study, we present models of time series with time granulation that reduce representation errors and subsequently give rise to the better approximation abilities and ...
Rami Al-Hmouz, Witold Pedrycz
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1987
The concept of a stationary time series was, apparently, formalized by Khintchine in 1932. An infinite sequence y(t), t = 0, + 1, …, of random variables is called stationary if the joint probability law of y(t1), y(t2), …, y(tn) is the same as that of y(t1+t) …, y(tn +1) for any integers, t1, t2, …, tn, t and any n.
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The concept of a stationary time series was, apparently, formalized by Khintchine in 1932. An infinite sequence y(t), t = 0, + 1, …, of random variables is called stationary if the joint probability law of y(t1), y(t2), …, y(tn) is the same as that of y(t1+t) …, y(tn +1) for any integers, t1, t2, …, tn, t and any n.
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Mining Time Series with Mine Time
2006We present, Mine Time, a tool that supports discovery over time series data. Mine Time is realized by the introduction of novel algorithmic processes, which support assessment of coherence and similarity across timeseries data. The innovation comes from the inclusion of specific ‘control' operations in the elaborated time-series matching metric.
Lefteris Koumakis +3 more
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A Framework for Time-Series Analysis
2010The popularity of time-series databases in many applications has created an increasing demand for performing data-mining tasks (classification, clustering, outlier detection, etc.) on time-series data. Currently, however, no single system or library exists that specializes on providing efficient implementations of data-mining techniques for time-series
Vladimir Kurbalija +3 more
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nonlinear time series analysis [PDF]
, 2008 Since the early 1980s, there has been a growing interest in stochastic nonlinear dynamical systems of the form, where is a zero mean, covariance stationary process, is the conditional volatility, and is an independent and identically distributed noise process.
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A Review on Outlier/Anomaly Detection in Time Series Data
ACM Computing Surveys, 2022U Mori +2 more
exaly
An Experimental Review on Deep Learning Architectures for Time Series Forecasting
International Journal of Neural Systems, 2021Manuel Carranza-García +2 more
exaly