Finding the embedding dimension and variable dependencies in time series

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Finding the embedding dimension and variable dependencies in time series. / Pi, Hong; Peterson, Carsten.

I: Neural Computation, Vol. 6, Nr. 3, 1994, s. 509-520.

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

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Pi, Hong ; Peterson, Carsten. / Finding the embedding dimension and variable dependencies in time series. I: Neural Computation. 1994 ; Vol. 6, Nr. 3. s. 509-520.

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TY - JOUR

T1 - Finding the embedding dimension and variable dependencies in time series

AU - Pi, Hong

AU - Peterson, Carsten

PY - 1994

Y1 - 1994

N2 - We present a general method, the δ-test, which establishes functional dependencies given a sequence of measurements. The approach is based on calculating conditional probabilities from vector component distances. Imposing the requirement of continuity of the underlying function, the obtained values of the conditional probabilities carry information on the embedding dimension and variable dependencies. The power of the method is illustrated on synthetic time-series with different time-lag dependencies and noise levels and on the sunspot data. The virtue of the method for preprocessing data in the context of feedforward neural networks is demonstrated. Also, its applicability for tracking residual errors in output units is stressed.

AB - We present a general method, the δ-test, which establishes functional dependencies given a sequence of measurements. The approach is based on calculating conditional probabilities from vector component distances. Imposing the requirement of continuity of the underlying function, the obtained values of the conditional probabilities carry information on the embedding dimension and variable dependencies. The power of the method is illustrated on synthetic time-series with different time-lag dependencies and noise levels and on the sunspot data. The virtue of the method for preprocessing data in the context of feedforward neural networks is demonstrated. Also, its applicability for tracking residual errors in output units is stressed.

U2 - 10.1162/neco.1994.6.3.509

DO - 10.1162/neco.1994.6.3.509

M3 - Article

VL - 6

SP - 509

EP - 520

JO - Neural Computation

JF - Neural Computation

SN - 1530-888X

IS - 3

ER -