10,431 research outputs found
Forecasting the CATS benchmark with the Double Vector Quantization method
The Double Vector Quantization method, a long-term forecasting method based
on the SOM algorithm, has been used to predict the 100 missing values of the
CATS competition data set. An analysis of the proposed time series is provided
to estimate the dimension of the auto-regressive part of this nonlinear
auto-regressive forecasting method. Based on this analysis experimental results
using the Double Vector Quantization (DVQ) method are presented and discussed.
As one of the features of the DVQ method is its ability to predict scalars as
well as vectors of values, the number of iterative predictions needed to reach
the prediction horizon is further observed. The method stability for the long
term allows obtaining reliable values for a rather long-term forecasting
horizon.Comment: Accepted for publication in Neurocomputing, Elsevie
A new self-organizing neural gas model based on Bregman divergences
In this paper, a new self-organizing neural gas model that we call Growing Hierarchical Bregman Neural
Gas (GHBNG) has been proposed. Our proposal is based on the Growing Hierarchical Neural Gas (GHNG) in which Bregman divergences are incorporated in order to compute the winning neuron. This model has been applied to anomaly detection in video sequences together with a Faster R-CNN as an object detector module. Experimental results not only confirm the effectiveness of the GHBNG for the detection of anomalous object in video sequences but also its selforganization
capabilities.Universidad de Málaga. Campus de Excelencia Internacional AndalucĂa Tec
Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation
We propose in this paper an exploratory analysis algorithm for functional
data. The method partitions a set of functions into clusters and represents
each cluster by a simple prototype (e.g., piecewise constant). The total number
of segments in the prototypes, , is chosen by the user and optimally
distributed among the clusters via two dynamic programming algorithms. The
practical relevance of the method is shown on two real world datasets
Modeling Financial Time Series with Artificial Neural Networks
Financial time series convey the decisions and actions of a population of human actors over time. Econometric and regressive models have been developed in the past decades for analyzing these time series. More recently, biologically inspired artificial neural network models have been shown to overcome some of the main challenges of traditional techniques by better exploiting the non-linear, non-stationary, and oscillatory nature of noisy, chaotic human interactions. This review paper explores the options, benefits, and weaknesses of the various forms of artificial neural networks as compared with regression techniques in the field of financial time series analysis.CELEST, a National Science Foundation Science of Learning Center (SBE-0354378); SyNAPSE program of the Defense Advanced Research Project Agency (HR001109-03-0001
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