This document summarizes a presentation on mathematical modeling using self-organizing maps (SOM) and its application to stock price prediction. It introduces SOM as an unsupervised neural network that can reduce dimensions and display similarities in data. The presentation describes how SOM works through competitive learning and updates node weights. It then discusses how SOM can be used for modeling, prediction, regional data analysis, and more. As an example application, it summarizes a study that used a hybrid SOM-multilayer perceptron model to more accurately predict stock prices of Lucent Inc. over five years compared to SOM or backpropagation neural networks alone. The conclusion states that techniques like SOM can enhance modeling and that collaborating

1. Mathematical Modeling
Self Organizing Map
Overview and Application in Prediction
Presented By
Decky Aspandi Latif
56070701073
Saturday, November 23, 2013

2. Layout
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Introduction
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SOM in Brief
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Basic of SOM
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SOM in Modeling and Prediction
Application of SOM in Stock Prices
Prediction
Conclusion
Saturday, November 23, 2013

3. Introduction
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Currently, great need emerges for better
techniques, tools and practices.
Modeling could be applied to various area →
minimize cost & Optimization
Self Organizing Map → ANN(connectionist
paradigms) → support and changes in
approaches & modeling technique
Disparate data analysis in 2 scales, regional and
global.
Saturday, November 23, 2013

4. Self Organizing Map
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Proposed by Tuevo Kohonen (1972)
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Unsupervised Neural Network
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Data driven learning process
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Reduce dimensions,display similarities
Saturday, November 23, 2013

5. SOM (Cont..)
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Mapping Nodes to group of class
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Selection of Best Matching Unit
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Cooperative Learning
Algorithm :
2. choose random vector
3. examined & select BMU
4. Calculate Neighbourhood
5. Update appropriate weights
6. Repeat step 2 for N times
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Y, Red, Elevation,..
1. Initialize weight of nodes
X, Blue, Density,..

6. SOM → Modeling
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Clustering Capability
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Modeling & Prediction
Prediction
Saturday, November 23, 2013
Ecological
Modeling
Regional Data
Analysis

7. Application → Prediction
“ Predicting Stock Prices Using a Hybrid Kohonen Self Organizing
Map (SOM) “ , Mark & Olatoyosi, 2007
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Main aim → Stock Prices Prediction
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Applied on LucentI Inc, using five years data → 1251 points
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Hybridization of SOM with Multilayer Perceptron
Saturday, November 23, 2013

8. HSOM → Prediction (cont)
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Flow of Process
Net Configuration
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9. HSOM → Prediction (cont)
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Hybrid HSOM outperform SOM & BPN
BPN comes inaccurate when price > $60 →
Significant Loss in investment
HSOM has lowest error
(0~12) → Increase in return
of Investment (ROI)
Saturday, November 23, 2013

10. Conclusion
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ANN can be used to enhance and alter the
modeling technique
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SOM is an Unsupervised Neural Network
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Clustering classes with mapping nodes
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Various application of SOM on Modeling &
Simulation → prediction
By collaborating SOM with other method →
greater results.
Saturday, November 23, 2013

11. The End.
Thank You.
Saturday, November 23, 2013
Decky Aspandi Latif
56070701073