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1
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34548696055
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Some previous work on using various criteria as guides to a good reconstruction space include A. Fraser and H. Swinney, Phys. Rev. A 33 (1986) 1134;
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(1986)
Phys. Rev. A
, vol.33
, pp. 1134
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Fraser, A.1
Swinney, H.2
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2
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0001628622
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A. Fraser, Physica D 34 (1989) 391, who use informational criteria;
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(1989)
Physica D
, vol.34
, pp. 391
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Fraser, A.1
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3
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44949273552
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M. Casdagli, S. Eubank, J. Farmer and J. Gibson Physica D 51 (1991) 52 use geometrical criteria such as minimal distortion to indicate a good reconstruction. These references differ from our work, in the criteria used for optimality, and in the range of possibilities for the reconstruction directions.
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(1991)
Physica D
, vol.51
, pp. 52
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Casdagli, M.1
Eubank, S.2
Farmer, J.3
Gibson, J.4
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4
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0000779360
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D. Rand and L. Young, eds. Springer, Berlin
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F. Takens, in: Dynamical Systems and Turbulence, D. Rand and L. Young, eds. (Springer, Berlin, 1981) p. 366.
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(1981)
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Takens, F.1
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5
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0003621624
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A Test for Independence Based on the Correlation Dimension
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SSRI, University of Wisconsin
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W Brock, W. Dechert and J. Scheinkman, A Test for Independence Based on the Correlation Dimension, Technical Report 8702, SSRI, University of Wisconsin (1987).
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(1987)
Technical Report 8702
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Brock, W.1
Dechert, W.2
Scheinkman, J.3
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10
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0000314757
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Using surrogate data to detect nonlinearity in time series
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eds. M. Casdagli and S. Eubank Addison-Wesley, Reading, MA
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J. Theiler, B. Galdrikian, A. Longtin, S. Eubank and J. Farmer, Using surrogate data to detect nonlinearity in time series, in: Nonlinear Modeling and Forecasting, eds. M. Casdagli and S. Eubank (Addison-Wesley, Reading, MA, 1992) p. 163;
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(1992)
Nonlinear Modeling and Forecasting
, pp. 163
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Theiler, J.1
Galdrikian, B.2
Longtin, A.3
Eubank, S.4
Farmer, J.5
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11
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44049111332
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J. Theiler, S. Eubank, A. Longtin, B. Galdrikian and J. Farmer, Physica D 58 (1992) 77.
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(1992)
Physica D
, vol.58
, pp. 77
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Theiler, J.1
Eubank, S.2
Longtin, A.3
Galdrikian, B.4
Farmer, J.5
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12
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0001904039
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The future of time series: Learning and understanding
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eds. A. Weigend and N. Gershenfeld, Santa Fe Institute Studies in the Sciences of Complexity Addison-Wesley
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N. Gershenfeld and A. Weigend, The future of time series: learning and understanding, in: Time Series Prediction: Forecasting the Future and Understanding the Past, eds. A. Weigend and N. Gershenfeld, Santa Fe Institute Studies in the Sciences of Complexity (Addison-Wesley, 1993), especially p. 10.
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, pp. 10
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Gershenfeld, N.1
Weigend, A.2
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