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- W4414904 abstract "This chapter focuses on the autoregressive-moving average (ARMA) modeling of the nonperiodic component and pattern classification of short time series (TS) human plasma. The periodic component is found by periodogram analysis. The perturbation is then obtained by subtracting significant periodic component from the original TS. The perturbations can be modelled by an ARMA process. This model reveals the correlation between the present perturbation and those in the past. The pattern of each TS can be represented by the mean, standard deviation of x(t), the intensity of the sinusoidal function, and the coefficients of the ARMA process. These patterns reveal the model of the TS, and it can also be used to predict future values and as a source of features for classification processes. The ARMA modeling of the TS has been extensively applied in many fields, such as economics, engineering, and astronomy." @default.
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- W4414904 date "1978-01-01" @default.
- W4414904 modified "2023-09-25" @default.
- W4414904 title "ARMA MODELING OF THE NON-PERIODIC COMPONENT AND PATTERN CLASSIFICATION OF SHORT TIME SERIES FROM HUMAN PLASMA" @default.
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- W4414904 doi "https://doi.org/10.1016/b978-0-08-022678-1.50054-0" @default.
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