1,353 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
Neural networks in geophysical applications
Neural networks are increasingly popular in geophysics.
Because they are universal approximators, these
tools can approximate any continuous function with an
arbitrary precision. Hence, they may yield important
contributions to finding solutions to a variety of geophysical applications.
However, knowledge of many methods and techniques
recently developed to increase the performance
and to facilitate the use of neural networks does not seem
to be widespread in the geophysical community. Therefore,
the power of these tools has not yet been explored to
their full extent. In this paper, techniques are described
for faster training, better overall performance, i.e., generalization,and the automatic estimation of network size
and architecture
Representation of Functional Data in Neural Networks
Functional Data Analysis (FDA) is an extension of traditional data analysis
to functional data, for example spectra, temporal series, spatio-temporal
images, gesture recognition data, etc. Functional data are rarely known in
practice; usually a regular or irregular sampling is known. For this reason,
some processing is needed in order to benefit from the smooth character of
functional data in the analysis methods. This paper shows how to extend the
Radial-Basis Function Networks (RBFN) and Multi-Layer Perceptron (MLP) models
to functional data inputs, in particular when the latter are known through
lists of input-output pairs. Various possibilities for functional processing
are discussed, including the projection on smooth bases, Functional Principal
Component Analysis, functional centering and reduction, and the use of
differential operators. It is shown how to incorporate these functional
processing into the RBFN and MLP models. The functional approach is illustrated
on a benchmark of spectrometric data analysis.Comment: Also available online from:
http://www.sciencedirect.com/science/journal/0925231
Using a Machine Learning Approach to Model a Chatbot for Ceylon Electricity Board Website
Customer support is one of the main aspects of the user experience for online services. However, the rise of natural language processing techniques, the industry is looking at automated chatbot solutions to provide quality services to an ever-growing user base. In Sri Lanka, Ceylon Electricity Board website is one of the largest websites that customers use always to get information about electricity services. Hence, a chatbot system is very essential in CEB website. This paper presents a study about implementing and evaluating of a chatbot model for CEB website. This study implements virtual conversation agent based on deep learning algorithm which is multilayer perceptron neural network and a special text dataset for conversations about CEB services. The conversation agent model is made by utilizing the natural language processing techniques to facilitate the processing of user messages. The output of this research is the response from the chatbot and identify the best testing method to get highest accuracy for chatbot model. The chatbot model achieves the highest accuracy with the number of epochs set to 2000 and the learning rate value of 0.01 on response context data training so that it gets 78.8% accuracy.
Keywords: Natural language processing, chatbot, deep learning, multilayer perceptron neural network, Monte Carlo cross validation, k-fold cross validatio
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