Finding the embedding dimension and variable dependencies in time series

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Abstract

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.

Detaljer

Författare
  • Hong Pi
  • Carsten Peterson
Enheter & grupper
Externa organisationer
  • Lund University
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Annan fysik
Originalspråkengelska
Sidor (från-till)509-520
Antal sidor11
TidskriftNeural Computation
Volym6
Utgåva nummer3
StatusPublished - 1994
PublikationskategoriForskning
Peer review utfördJa