Forecasting 2001- Neural Networks

Forecasting 2001- Neural Networks - Forecasting Financial...

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Copyright © 1999-2006 Investment Analytics Forecasting Financial Markets – Neural Networks Slide: 1 Forecasting Financial Markets Neural Networks Copyright © 1999-2006 Investment Analytics
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Copyright © 1999-2006 Investment Analytics Forecasting Financial Markets – Neural Networks Slide: 2 Overview ¾ Overview of neural networks ¾ Design considerations ¾ Applications
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Copyright © 1999-2006 Investment Analytics Forecasting Financial Markets – Neural Networks Slide: 3 A Neural Network ¾ Processing elements ± Neurons Receives & processes input(s) Delivers single output ¾ Network ± Collection of interlinked neurons ± Grouped in layers Input Intermediate (hidden) Output
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Copyright © 1999-2006 Investment Analytics Forecasting Financial Markets – Neural Networks Slide: 4 A Schematic Diagram of a Neuron
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Copyright © 1999-2006 Investment Analytics Forecasting Financial Markets – Neural Networks Slide: 5 The Neuron Analogy
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Copyright © 1999-2006 Investment Analytics Forecasting Financial Markets – Neural Networks Slide: 6 A 3 Layer Neural Network Network Inputs Hidden Layer Network Outputs
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Copyright © 1999-2006 Investment Analytics Forecasting Financial Markets – Neural Networks Slide: 7 Processing Information ¾ Inputs ± Correspond to single attributes Can include qualitative data ¾ Outputs ± Solution to a problem E.g. forecast, or binary value ¾ Weights ± Express relative importance of data On inputs or data transferred between layers ± “Learning” = adapting weights
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Copyright © 1999-2006 Investment Analytics Forecasting Financial Markets – Neural Networks Slide: 8 Activation Function ¾ Determines whether a neuron will “fire” ± I.E. Produce an output ¾ Weighted sum of inputs ± For N inputs i into neuron j = = N i i ij j X W Y 1
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Copyright © 1999-2006 Investment Analytics Forecasting Financial Markets – Neural Networks Slide: 9 Transfer Function ¾ Transforms or normalizes output ± Also called a transformation or squashing function ¾ Popular choice ± Sigmoid : f(x) = 1/(1+e -x ) ¾ Alternative: threshold detector / hard limiter ± E.G. F(Y j ) in range {0, 1} if Y j > 0.5, 0 otherwise
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Copyright © 1999-2006 Investment Analytics Forecasting Financial Markets – Neural Networks Slide: 10 Architecture / Network Topology ¾ Number of neurons ¾ Number of hidden layers ¾ Connections ± Feed forward/backwards ± Fully or partially connected ¾ Static or adaptive architecture
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Copyright © 1999-2006 Investment Analytics Forecasting Financial Markets – Neural Networks Slide: 11 Learning ¾ Supervised ± Uses set of inputs for which desired output is known ± Cost function f(desired-actual) used to change weights ± Example: Hopfield network ¾ Unsupervised ± Network shown only inputs ± No information on “correct” outputs ± Self-organizing ± Example: Kohonen self-organizing feature maps
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Copyright © 1999-2006 Investment Analytics Forecasting Financial Markets – Neural Networks
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Forecasting 2001- Neural Networks - Forecasting Financial...

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