9,488 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
A Hierarchical Emotion Regulated Sensorimotor Model: Case Studies
Inspired by the hierarchical cognitive architecture and the perception-action
model (PAM), we propose that the internal status acts as a kind of
common-coding representation which affects, mediates and even regulates the
sensorimotor behaviours. These regulation can be depicted in the Bayesian
framework, that is why cognitive agents are able to generate behaviours with
subtle differences according to their emotion or recognize the emotion by
perception. A novel recurrent neural network called recurrent neural network
with parametric bias units (RNNPB) runs in three modes, constructing a
two-level emotion regulated learning model, was further applied to testify this
theory in two different cases.Comment: Accepted at The 5th International Conference on Data-Driven Control
and Learning Systems. 201
Integer Echo State Networks: Hyperdimensional Reservoir Computing
We propose an approximation of Echo State Networks (ESN) that can be
efficiently implemented on digital hardware based on the mathematics of
hyperdimensional computing. The reservoir of the proposed Integer Echo State
Network (intESN) is a vector containing only n-bits integers (where n<8 is
normally sufficient for a satisfactory performance). The recurrent matrix
multiplication is replaced with an efficient cyclic shift operation. The intESN
architecture is verified with typical tasks in reservoir computing: memorizing
of a sequence of inputs; classifying time-series; learning dynamic processes.
Such an architecture results in dramatic improvements in memory footprint and
computational efficiency, with minimal performance loss.Comment: 10 pages, 10 figures, 1 tabl
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