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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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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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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 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, 2022Ane Blázquez-García +2 more
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An Experimental Review on Deep Learning Architectures for Time Series Forecasting
International Journal of Neural Systems, 2021Pedro Lara-Benítez +2 more
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Long sequence time-series forecasting with deep learning: A survey
Information Fusion, 2023Hongjun Wang, Chongshou Li
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