Results 31 to 40 of about 1,758,719 (322)
Estimation error for blind Gaussian time series prediction [PDF]
, 2011 We tackle the issue of the blind prediction of a Gaussian time series. For this, we construct a projection operator build by plugging an empirical covariance estimation into a Schur complement decomposition of the projector. This operator is then used to
Espinasse, Thibault +2 more
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Time Series Prediction on Population Dynamics [PDF]
E3S Web of ConferencesPredicting the time series is a challenging topic mainly on the era of big data. In this research, data taken from population dynamics of one dimension of logistic map with various parameters that leading the system into chaos.
Dwipayana I. Made Eka
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Time series forecasting by means of evolutionary algorithms [PDF]
, 2007 IEEE International Parallel and Distributed Processing Symposium. Long Beach, CA, 26-30 March 2007Many physical and artificial phenomena can be described by time series. The prediction of such phenomenon could be as complex as interesting. There are many
Isasi, Pedro +2 more
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Optimal model-free prediction from multivariate time series [PDF]
, 2015 Forecasting a time series from multivariate predictors constitutes a challenging problem, especially using model-free approaches. Most techniques, such as nearest-neighbor prediction, quickly suffer from the curse of dimensionality and overfitting for ...
Donner, Reik V. +2 more
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A combined robust fuzzy time series method for prediction of time series [PDF]
Neurocomputing, 2017 Outlier(s) have an adverse impact on the performance of fuzzy time series models.We proposed a combined robust fuzzy time series model (C-R-FTSM).C-R-FTSM uses fuzzy inputs composed of membership values as well as the crisp data.Training process of C-R-FTSM is performed by PSO in a single optimization process.Huber's loss function based on M estimator ...
Yolcu, Ozge Cagcag, Lam, Hak-Keung
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Radial Basis Function Nets for Time Series Prediction [PDF]
International Journal of Computational Intelligence Systems, 2009 This paper introduces a novel ensemble learning approach based on recurrent radial basis function networks (RRBFN) for time series prediction with the aim of increasing the prediction accuracy. Standing for the base learner in this ensemble, the adaptive
Abdelhamid Bouchachia
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Financial time series prediction using spiking neural networks. [PDF]
PLoS ONE, 2014 In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction.
David Reid +2 more
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Multi-step learning rule for recurrent neural models: an application to time series forecasting [PDF]
, 2001 Multi-step prediction is a difficult task that has attracted increasing interest in recent years. It tries to achieve predictions several steps ahead into the future starting from current information.
Galván, Inés M., Isasi, Pedro
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Linear Prediction of Long-Range Dependent Time Series [PDF]
, 2007 We present two approaches for next step linear prediction of long memory time series. The first is based on the truncation of the Wiener-Kolmogorov predictor by restricting the observations to the last $k$ terms, which are the only available values in ...
Godet, Fanny
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Gender Prediction by Gait Analysis Based on Time Series Variation of Joint Positions [PDF]
Journal of Systemics, Cybernetics and Informatics, 2015 In this paper, a novel gender prediction scheme based on a gait analysis is proposed. For the gait analysis, we propose a novel feature extraction scheme that uses the time series vari- ation in the joint positions directly. Here, normalization by linear
Ryusuke Miyamoto, Risako Aoki
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