A. S. Weigend and N.A. Gershenfeld, "Times Series Prediction: Forcasting the future and Understanding the Past", Addison-Wesley Publishing Company, 1994. Home/Search Document Not in Database Summary Related Articles Check |
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Analysis of Switching Dynamics with Competing Support Vector.. - Chang, Lin, Weng (2002) (Correct)
....problem we set the # = 1 and leave the other parameters the same as in the previous example. As can be seen in Figure 3, the underlying dynamics are well segmented. C. Santa Fe Time Series: Data Set D We also consider the benchmark Data Set D from the Santa Fe Time Series Prediction Competition [27]. This is an artificial data generated from a nine dimensional periodically driven system with an asymmetrical four well potential and a drift on the parameters. The system operates in one well for Fig. 2. First six iterations (data with noise) some time and then switch to another well with a ....
A. S. Weigend and N. A. Gershenfeld, editors. Time series prediction: forcasting the future and understanding the past. Addison-Wesley, 1994.
Width optimization of the Gaussian kernels in.. - Benoudjit.. (2002) (Correct)
....mean square error, we find an optimal width scaling factor for small q (figure 11) Finally, RBF networks can be used for time series prediction. The principle consists in predicting the next value in the sequence, as a function of the previous values. A well known time example is the SantaFe A [13]. In this sequence the last six values are used to predict the new one. In figure 12, one can notice that a minimal MSE V is obtained for a value of 13 of the width scaling factor. It should be mentioned, that, in this case, no local decomposition of the function seems to appear. Indeed, the ....
A. S. Weigend and N.A. Gershenfeld, "Times Series Prediction: Forcasting the future and Understanding the Past", Addison-Wesley Publishing Company, 1994.
Modelling the Wiener Cascade using Time delayed.. - Wagner, Thompson, .. (1995) (Correct)
.... Delayed Networks, Recurrent Networks, Nonlinear Dynamic Systems, Generating Series, Volterra Series January 30, 1995 1 Introduction Considerable success has been reported when approximating dynamic systems using either Time Delayed Neural Networks (TDNN) or Recurrent Neural Networks (RNN) (Weigend and Gershenfeld 1993). While both techniques are frequently capable of producing an excellent approximation to a training data set, their generalisation ability cannot be guaranteed. This may be due to the networks not learning the underlying structure of the system. In this article the Volterra and Generating Series ....
Weigend, A. S. and N. A. Gershenfeld (1993). Time series prediction : Forcasting the future and understanding the Past. Addison-Wesley.
Efficient Algorithms for Function Approximation with.. - Don R. Hush, Bill Horne (1996) (9 citations) (Correct)
....by the IIA SweepingHinge algorithm. This experiment can be viewed as an empirical test of Barron s theoretical bound on approximation error. The third experiment involves a nonlinear time series prediction problem using real data from the Santa Fe Institute Time Series Prediction Competition [50]. All of the results presented in this section used the IIA algorithm to build a one hidden layer network model, with SweepingHinge employed at Step 2 of the algorithm. N min was set equal to 3d throughout, where d is the input dimension. This proved to be sufficient to maintain both stable and ....
....effect of the estimation error is noticeable in these curves as they start to bend over around n = 10 nodes. Again however, they show no real dependence on the dimension d. The final experiment in this section uses real data from the Santa Fe Institute s Time Series Prediction Competition [50]. In this experiment we develop a predictive model for the time series in Data Set A from the competition. This data is obtained from the emissions of an NH 3 F IR laser and is known to have a Lorenz like chaotic attractor [27] The experiment performed here is similar to that carried out in ....
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A.S. Weigend and N.A. Gershenfeld. Time series Prediction: Forcasting the Future and Understanding the Past. Santa Fe Institute Studies in the Sciences of Complexity. Addison--Wesley, Redding, MA, 1994.
Fast Approximation of the Bootstrap for Model Selection - Simon, Lendasse, Wertz.. (2003) (Correct)
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A. S. Weigend and N.A. Gershenfeld, "Times Series Prediction: Forcasting the future and Understanding the Past", Addison-Wesley Publishing Company, 1994.
Double SOM for long-term time series prediction - Geoffroy Simon Amaury (2003) (Correct)
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A. S. Weigend and N.A. Gershenfeld, "Times Series Prediction: Forcasting the future and Understanding the Past", Addison-Wesley Publishing Company, 1994.
Forecasting Financial Time Series through Intrinsic Dimension .. - Verleysen, al. (1999) (Correct)
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Weigend A. S., Gershenfeld N.A.: Times Series Prediction: Forcasting the future and Understanding the Past. Addison-Wesley Publishing Company (1994)
The Future of Chess-Playing Technologies and the Significance of.. - DeCoste (1997) (Correct)
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Andreas S. Weigend and Neil A. Gershenfeld, editors. Time Series Prediction: Forcasting the Future and Understanding the Past. Addison-Wesley, 1994.
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