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Finding the embedding dimension and variable dependencies in time series

Pi, Hong and Peterson, Carsten LU (1994) In Neural Computation 6(3). p.509-520
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.
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author
and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Neural Computation
volume
6
issue
3
pages
11 pages
publisher
MIT Press
ISSN
1530-888X
DOI
10.1162/neco.1994.6.3.509
language
English
LU publication?
yes
id
56b85c76-d63b-4209-b417-ba5cfdc4ef81
date added to LUP
2019-05-31 16:29:49
date last changed
2019-08-07 10:38:52
@article{56b85c76-d63b-4209-b417-ba5cfdc4ef81,
  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.}},
  author       = {{Pi, Hong and Peterson, Carsten}},
  issn         = {{1530-888X}},
  language     = {{eng}},
  number       = {{3}},
  pages        = {{509--520}},
  publisher    = {{MIT Press}},
  series       = {{Neural Computation}},
  title        = {{Finding the embedding dimension and variable dependencies in time series}},
  url          = {{http://dx.doi.org/10.1162/neco.1994.6.3.509}},
  doi          = {{10.1162/neco.1994.6.3.509}},
  volume       = {{6}},
  year         = {{1994}},
}