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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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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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Time Series: Theory and Methods.
The Statistician, 19893. Time Series: Theory and Methods. By P. J. Brockwell and R. A. Davis. ISBN 0 387 96406 1. Springer, New York, 1987. x + 520pp. DM 120.
Eric R. Ziegel, P. Brockwell, R. Davis
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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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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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A Review on Outlier/Anomaly Detection in Time Series Data
ACM Computing Surveys, 2022Usue Mori Carrascal +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