2,633 research outputs found
Training Echo State Networks with Regularization through Dimensionality Reduction
In this paper we introduce a new framework to train an Echo State Network to
predict real valued time-series. The method consists in projecting the output
of the internal layer of the network on a space with lower dimensionality,
before training the output layer to learn the target task. Notably, we enforce
a regularization constraint that leads to better generalization capabilities.
We evaluate the performances of our approach on several benchmark tests, using
different techniques to train the readout of the network, achieving superior
predictive performance when using the proposed framework. Finally, we provide
an insight on the effectiveness of the implemented mechanics through a
visualization of the trajectory in the phase space and relying on the
methodologies of nonlinear time-series analysis. By applying our method on well
known chaotic systems, we provide evidence that the lower dimensional embedding
retains the dynamical properties of the underlying system better than the
full-dimensional internal states of the network
Bidirectional deep-readout echo state networks
We propose a deep architecture for the classification of multivariate time
series. By means of a recurrent and untrained reservoir we generate a vectorial
representation that embeds temporal relationships in the data. To improve the
memorization capability, we implement a bidirectional reservoir, whose last
state captures also past dependencies in the input. We apply dimensionality
reduction to the final reservoir states to obtain compressed fixed size
representations of the time series. These are subsequently fed into a deep
feedforward network trained to perform the final classification. We test our
architecture on benchmark datasets and on a real-world use-case of blood
samples classification. Results show that our method performs better than a
standard echo state network and, at the same time, achieves results comparable
to a fully-trained recurrent network, but with a faster training
Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data
Similarity-based approaches represent a promising direction for time series
analysis. However, many such methods rely on parameter tuning, and some have
shortcomings if the time series are multivariate (MTS), due to dependencies
between attributes, or the time series contain missing data. In this paper, we
address these challenges within the powerful context of kernel methods by
proposing the robust \emph{time series cluster kernel} (TCK). The approach
taken leverages the missing data handling properties of Gaussian mixture models
(GMM) augmented with informative prior distributions. An ensemble learning
approach is exploited to ensure robustness to parameters by combining the
clustering results of many GMM to form the final kernel.
We evaluate the TCK on synthetic and real data and compare to other
state-of-the-art techniques. The experimental results demonstrate that the TCK
is robust to parameter choices, provides competitive results for MTS without
missing data and outstanding results for missing data.Comment: 23 pages, 6 figure
The Revolution in Islamic Finance
Commercial interests from all religious backgrounds have a vested interest in holding the world together, and it is no surprise that global banking is emerging as an arena for cooperation between Muslim and Western business leaders even as their politicians and citizens seem to drift further apart. Western nations have many good reasons for encouraging the success of Islamic banking and smoothing its integration into a more stable global economy. Progress toward financial harmonization could pave the way for cooperation on other issues, including the far more contentious fields of politics and security. Islamic finance will need all of the international assistance it can solicit because it is at a crossroads that will force the entire movement to reinvent itself. Five years from now, its landscape will be unrecognizable to the cluster of institutions and personalities that view themselves as today\u27s unassailable industry leaders. This Article examines mounting pressures that are driving the current revolution in Islamic finance. Islamic bankers must now adapt to simultaneous challenges on three fronts: integration with the global financial system; coordination with the leading international organizations of the Islamic world; and penetration of mass markets in dozens of countries with conflicting cultural, political, and economic conditions. To complicate matters, all of these audiences are moving targets, experiencing upheavals at least as profound as the metamorphosis of Islamic finance itself. [CONT
HACking at Non-linearity: Evidence from Stocks and Bonds
The implicit assumption of linearity is an important element in empirical finance. This study presents a hypothesis testing approach which examines the linear behaviour of the conditional mean between stock and bond returns. Conventional tests detect spurious non-linearity in the conditional mean caused by heteroskedasticity and/or autocorrelation. This study re-states these tests in a heteroskedasticity and autocorrelation consistent (HAC) framework and we find that stock and bond returns are indeed linear-in-the-mean in both univariate and bivariate settings. This study contends that previous research may have detected spurious non-linearity due to size distortions caused by heteroskedasticity and autocorrelation, rather than the presence of genuine non-linearity.linearity, nonlinear, heteroskedasticity-robust tests, autocorrelation-robust tests
Deep Divergence-Based Approach to Clustering
A promising direction in deep learning research consists in learning
representations and simultaneously discovering cluster structure in unlabeled
data by optimizing a discriminative loss function. As opposed to supervised
deep learning, this line of research is in its infancy, and how to design and
optimize suitable loss functions to train deep neural networks for clustering
is still an open question. Our contribution to this emerging field is a new
deep clustering network that leverages the discriminative power of
information-theoretic divergence measures, which have been shown to be
effective in traditional clustering. We propose a novel loss function that
incorporates geometric regularization constraints, thus avoiding degenerate
structures of the resulting clustering partition. Experiments on synthetic
benchmarks and real datasets show that the proposed network achieves
competitive performance with respect to other state-of-the-art methods, scales
well to large datasets, and does not require pre-training steps
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