From: omlinc@williams.cs.rpi.edu (C Omlin)
Subject: references on time series prediction
Organization: Rensselaer Polytechnic Institute, Troy NY
Date: Mon, 3 Aug 1992 19:36:26 GMT

Hi !

Here is a summary of replies I have received in response
to my request for references on time series prediction using
artificial neural networks. 

Thanks to all those who replied !

Christian

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From ellner@stat.ncsu.edu Fri Jul 31 10:53:22 1992

1. (Just in case) see the book Nonlinear Modeling and Forecasting
   (ed. M.Casdagli and S. Eubank), SFI Studies in the Sciences of
   Complexity, Proceedings Vol. XII, Addison-Wesley NY (1992). {lotsa
   net papers}


2. D. Nychka, S.Ellner, A.R. Gallant, and D. McCaffrey. 1992. 
   Finding chaos in noisy systems. J. Royal Statistical Society Ser. B vol.54,
   399-426.

3. D. McCaffrey, S. Ellner, A.R. Gallant, D. Nychka. 1992. Estimating
   the Lyapunov exponent of a chaotic system with nonparametric regression. 
   J. Amer. Stat. Assoc. (in press; to appear this Fall).


4. Wolpert, D.M. and R.C. Miall 1990. Detecting chaos with neural networks. 
   Proc. Roy. Soc. Lond. B 242, 82-86.

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From @computer-science-test.birmingham.ac.uk:A.Hooper-COG1@computer-science.birmingham.ac.uk Fri Jul 31 11:56:35 1992

//not a NN aproach but it compares traditional approaches with NN ones
[1] "Nonlinear Prediction of Chaotic Time Series",
Martin Casdagli, 
Physica D  35 (1989),
pp 335-356.


[2] "Predicting the Mackey-Glass Timeseries With Cascade-Correltion Learning",
R. Scott Crowder,III, 
In- "Proceedings of the 1990 Connectionist Models Summer School",
(editors) D. S. Touretzky, J. L. Elman, T. J. Sejnowski, and G. E. Hinton,
(Publisher) Morgan-Kauffman,
(Place) San Mateo, CA.,
pp 117-123.


[5] "Predicting the Future: A Connectionist Approach",
Andreas S. Wiegend,  Bernardo A. Huberman  and  David E. Rumelhart,
International Journal of Neural Systems, vol 1, 3 (1990),
pp 193-209. 


[6] "Nonlinear Forecasting as a Way of Distinguishing Chaos From Measurement Error in
Time Series",
George Sugihara, and Robert M. May,
Nature, 344, (1990),
pp 734-741.

[7] "Predicting Chaotic Time Series",
J. Doyne Farmer, and John J. Sidorwich,
Physical Review Letters, vol 59, 8 (1987),
pp 845-848.

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From ingber@umiacs.UMD.EDU Fri Jul 31 14:15:49 1992

I've written a paper specific to a large class of systems:
%A L. Ingber
%T Generic mesoscopic neural networks based on statistical mechanics
of neocortical interactions
%J Phys. Rev. A
%V 45
%S 4
%P R2183-R2186
%D 1992

This paper can be accessed electronically:

              Getting (P)Reprints via Anonymous Ftp

     Please  note  that  some of my (p)reprints can be downloaded
via anonymous ftp from ftp.umiacs.umd.edu [128.8.120.23]  in  the
pub/ingber  directory.   If  you  have problems with this, let me
know and I will be glad to prepare a uuencoded copy to  email  to
you.   Sorry,  but I cannot take on the task of mailing out hard-
copies.

     Just follow these procedures on your local machine:
local% ftp ftp.umiacs.umd.edu
[local% ftp 128.8.120.23]
Name (ftp.umiacs.umd.edu:yourloginname): anonymous
Password (ftp.umiacs.umd.edu:anonymous): [type in yourloginname]
ftp> cd pub/ingber
ftp> binary
ftp> get README.file
ftp> get file.ps.Z
ftp> quit
local% uncompress file.ps.Z
local% lpr [-P..] file.ps [to your PostScript laserprinter]

     With a uuencoded copy, first save to  mailfile.   Strip  out
lines  before  "begin"  and  after  "end".   Save to mailfile.uu.
Applying
local% uudecode mailfile.uu
will leave file.ps.Z.  Then, proceed as above.

===================================================================
From chadha@tree.egr.uh.edu Fri Jul 31 17:27:34 1992

Dear Christian,
		I am also interseted in time series prediction (both  
feedforward and recurrent with more emphasis on the latter) for being  
able to get an estimation of the underlying dynamics of a nonlinear  
system.
		I would appreciate it if you could forward the  
replies that you get to me(in case you are not summarizing for the  
net or if you think that it will be some time before you do that).

		Thanks

Deepak Chadha
Electrical Engineering 

University of Houston

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From Ranka@top.cis.syr.edu Fri Jul 31 17:52:43 1992

K. Chakraborty, C. K. Mohan and K.
Mehrotra, and S. Ranka, 
\it Modeling Multivariate Time Series Using Neural Networks
\sl Neural Networks,
\rm to appear
\rm (with K. Chakraborty, C. K. Mohan and K.
Mehrotra).

===================================================================
From akerberg@tree.egr.uh.edu Mon Aug  3 15:16:30 1992

J C Principe, Alok Rathie and J-M Kuo, Prediction of chaotic time series
with Neural Networks. I lost track of where this came from but a quick 

entry on a library computer should give you a complete reference. Another
alternative is to e-mail to principe@synapse.ee.ufl.edu
They use a Time Delay NN to predict and thus model a chaotic time series
(Mackey Glass time series), Nice paper

A.D. Back and A.C. Tsoi, FIR and IIR Synapses, a New Neural Network 

Architechture for Tine Series Modeling. Neural Computation 3, 375-385 

1991. A Multi layer Perceptron arhitechture is used with filters, IIR 

and FIR, instead of constant weights for synapses.

N.Z Hakim, J.J. Kaufman, G. Cerf and H.E. Meadows, A Discrete-Time Neural
Network Model for Systems Identification. Proceedings IJCNN-90. (IJCNN I
believe is the International Joint Conference on Neural Networks)
They use the "prediction error to train their model. Loosely related to
what you want.

N.Z Hakim, J.J. Kaufman, G. Cerf and H.E. Meadows, Nonlinear Time Series 

Prediction with a Discrete-Time Neural Network Model. Proceedings 

IJCNN-91. I'm not sure if the whole paper is published or only the  
abstract.
Recurrent NN used to predict e.g. the Mackey-Glass time series.

J.A. Villareal and Robert O. Shelton, A Space-Time Neural Network,
Pesented at the Second Annual Joint Conference on Neural Networks and 

Fuzzy Logic -90.