1,421 research outputs found
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
Nearly Optimal Sparse Group Testing
Group testing is the process of pooling arbitrary subsets from a set of
items so as to identify, with a minimal number of tests, a "small" subset of
defective items. In "classical" non-adaptive group testing, it is known
that when is substantially smaller than , tests are
both information-theoretically necessary and sufficient to guarantee recovery
with high probability. Group testing schemes in the literature meeting this
bound require most items to be tested times, and most tests
to incorporate items.
Motivated by physical considerations, we study group testing models in which
the testing procedure is constrained to be "sparse". Specifically, we consider
(separately) scenarios in which (a) items are finitely divisible and hence may
participate in at most tests; or (b) tests are
size-constrained to pool no more than items per test. For both
scenarios we provide information-theoretic lower bounds on the number of tests
required to guarantee high probability recovery. In both scenarios we provide
both randomized constructions (under both -error and zero-error
reconstruction guarantees) and explicit constructions of designs with
computationally efficient reconstruction algorithms that require a number of
tests that are optimal up to constant or small polynomial factors in some
regimes of and . The randomized design/reconstruction
algorithm in the -sized test scenario is universal -- independent of the
value of , as long as . We also investigate the effect of
unreliability/noise in test outcomes. For the full abstract, please see the
full text PDF
Sustainable Cooperative Coevolution with a Multi-Armed Bandit
This paper proposes a self-adaptation mechanism to manage the resources
allocated to the different species comprising a cooperative coevolutionary
algorithm. The proposed approach relies on a dynamic extension to the
well-known multi-armed bandit framework. At each iteration, the dynamic
multi-armed bandit makes a decision on which species to evolve for a
generation, using the history of progress made by the different species to
guide the decisions. We show experimentally, on a benchmark and a real-world
problem, that evolving the different populations at different paces allows not
only to identify solutions more rapidly, but also improves the capacity of
cooperative coevolution to solve more complex problems.Comment: Accepted at GECCO 201
An EMO Joint Pruning with Multiple Sub-networks: Fast and Effect
The network pruning algorithm based on evolutionary multi-objective (EMO) can
balance the pruning rate and performance of the network. However, its
population-based nature often suffers from the complex pruning optimization
space and the highly resource-consuming pruning structure verification process,
which limits its application. To this end, this paper proposes an EMO joint
pruning with multiple sub-networks (EMO-PMS) to reduce space complexity and
resource consumption. First, a divide-and-conquer EMO network pruning framework
is proposed, which decomposes the complex EMO pruning task on the whole network
into easier sub-tasks on multiple sub-networks. On the one hand, this
decomposition reduces the pruning optimization space and decreases the
optimization difficulty; on the other hand, the smaller network structure
converges faster, so the computational resource consumption of the proposed
algorithm is lower. Secondly, a sub-network training method based on
cross-network constraints is designed so that the sub-network can process the
features generated by the previous one through feature constraints. This method
allows sub-networks optimized independently to collaborate better and improves
the overall performance of the pruned network. Finally, a multiple sub-networks
joint pruning method based on EMO is proposed. For one thing, it can accurately
measure the feature processing capability of the sub-networks with the
pre-trained feature selector. For another, it can combine multi-objective
pruning results on multiple sub-networks through global performance impairment
ranking to design a joint pruning scheme. The proposed algorithm is validated
on three datasets with different challenging. Compared with fifteen advanced
pruning algorithms, the experiment results exhibit the effectiveness and
efficiency of the proposed algorithm
Big data clustering using grid computing and ant-based algorithm
Big data has the power to dramatically change the way institutes and organizations use their data. Transforming the massive amounts of data into knowledge will leverage the organizations performance to the maximum.Scientific and business organizations would benefit from utilizing big data. However, there are many challenges in dealing with big data such as storage, transfer, management and manipulation of big data.Many techniques are required to explore the hidden pattern inside the big data which have limitations in terms of hardware and software implementation. This paper presents a framework for big data clustering which utilizes grid technology and ant-based algorithm
Bio-inspired computation: where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques
Bio-inspired computation: where we stand and what's next
In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques
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