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Saturday, November 23, 2013

Mathematical Modeling

Self Organizing MapOverview and Application in Prediction

Presented By

Decky Aspandi Latif56070701073

Saturday, November 23, 2013

Layout

● Introduction● SOM in Brief

● Basic of SOM● SOM in Modeling and Prediction● Application of SOM in Stock Prices

Prediction● Conclusion

Saturday, November 23, 2013

Introduction● 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

Self Organizing Map

● Proposed by Tuevo Kohonen (1972)● Unsupervised Neural Network ● Data driven learning process● Reduce dimensions,display similarities

Saturday, November 23, 2013

SOM (Cont..)

● Mapping Nodes to group of class● Selection of Best Matching Unit● Cooperative LearningAlgorithm :

1. Initialize weight of nodes

2. choose random vector

3. examined & select BMU

4. Calculate Neighbourhood

5. Update appropriate weights

6. Repeat step 2 for N times

Y, R

ed, E

leva

tion

,..

X, Blue, Density,..

Y, R

ed, E

leva

tion

,..

X, Blue, Density,..

Saturday, November 23, 2013

SOM → Modeling

● Clustering Capability● Modeling & Prediction

EcologicalModeling

Regional Data Analysis

Prediction

Saturday, November 23, 2013

Application → Prediction

“ Predicting Stock Prices Using a Hybrid Kohonen Self Organizing Map (SOM) “ , Mark & Olatoyosi, 2007

● Main aim → Stock Prices Prediction

● Applied on LucentI Inc, using five years data → 1251 points

● Hybridization of SOM with Multilayer Perceptron

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HSOM → Prediction (cont)

● Flow of Process

Net Configuration

Saturday, November 23, 2013

HSOM → Prediction (cont)

● 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

Conclusion

● ANN can be used to enhance and alter the modeling technique

● SOM is an Unsupervised Neural Network● Clustering classes with mapping nodes● Various application of SOM on Modeling &

Simulation → prediction● By collaborating SOM with other method →

greater results.

Saturday, November 23, 2013

The End.

Thank You.

Decky Aspandi Latif56070701073


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