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Industrial Time Series Prediction
2018Time series prediction is a significant way for forecasting the variables involved in industrial process, which usually identifies the latent rules hidden behind the time series data of the variables by means of auto-regression. In this chapter we introduce the phase space reconstruction technique, which aims to construct the training dataset for ...
Jun Zhao, Wei Wang, Chunyang Sheng
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1983
The covariance matrix of a stacked data vector (y1′, y2′, ... yn′)′ of a meanzero weakly stationary process {yt} has a special structure: A submatrix Λ0 =Ey1y1′ is located along the main diagonal, the matrix \({\Lambda _\ell } = E{y_{\ell + 1}}{y'_1}\) along the \( \ell\)-th diagonal below the main diagonal, and \({\Lambda _{ - \ell }} = E{y_1}{y'_ ...
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The covariance matrix of a stacked data vector (y1′, y2′, ... yn′)′ of a meanzero weakly stationary process {yt} has a special structure: A submatrix Λ0 =Ey1y1′ is located along the main diagonal, the matrix \({\Lambda _\ell } = E{y_{\ell + 1}}{y'_1}\) along the \( \ell\)-th diagonal below the main diagonal, and \({\Lambda _{ - \ell }} = E{y_1}{y'_ ...
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2002
The problem of the prediction of time series belongs to the most important problems of the statistical inference of time series. There are many approaches to these problems, possibly the best known is that based on the Box-Jenkins methodology of modeling time series by using ARMA and ARIMA models, another approach is based on modeling time series by ...
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The problem of the prediction of time series belongs to the most important problems of the statistical inference of time series. There are many approaches to these problems, possibly the best known is that based on the Box-Jenkins methodology of modeling time series by using ARMA and ARIMA models, another approach is based on modeling time series by ...
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Incomplete electrocardiogram time series prediction
2016 IEEE Biomedical Circuits and Systems Conference (BioCAS), 2016The prevalence of Big Data has led to the operation practice based on time series data from multiple sources in many practical applications. The prediction analysis of time series, a fundamental objective of time series data crunch, is an integral part for planning and decision making.
Weiwei Shi +6 more
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Fuzzy Time Series Prediction Model
2011The main objective to design this proposed model is to overcome the drawbacks of the exiting approaches and derive more robust & accurate methodology to forecast data. This innovative soft computing time series model is designed by joint consideration of three key points (1) Event discretization of time series data (2 Frequency density based ...
Bindu Garg +3 more
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Interval prediction for chaotic time series.
2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
BORDIGNON, SILVANO, LISI, FRANCESCO
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Prediction of Multivariate Time Series
Journal of Applied Meteorology, 1964Abstract A method of linear prediction of stationary multivariate time series is discussed from the point of view of meteorological applications. Tests of significance are given and it is shown by examples that the method is practical even when the dimensionality of the series becomes quite large.
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Time after time: Factors predicting murder series' duration
Journal of Criminal Justice, 2022April Miin MiinChai +4 more
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