Jakarta Stock exchange forecasting using

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Runtun Waktu,” thesis, Program Studi S2 Ilmu Komputer, Yogyakarta: FMIPA UGM, ... Boediono and W. Koster, Teori dan Aplikasi Statistika dan Probabilitas, ...
SENG HANSUN

JAKARTA STOCK EXCHANGE FORECASTING USING BACKPROPAGATION NEURAL NETWORKS 2013 IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS TECHNOLOGY AND INDUSTRIAL DEVELOPMENT NUSA DUA, BALI - INDONESIA

INTRODUCTION

A time series is a set of regular time-ordered observations of a quantitative characteristic of an individual or collective phenomenon taken at successive periods.

GOAL Time series characteristics

Time series analysis

forecasting

ANN

Soft Computing

JKSE composite index data

BACKPROPAGATION NN MULTI-LAYER 3 MAIN NETWORKS STEPS

FEED-FORWARD PROCESS BACKPROPAGATION UPDATING WEIGHTS AND PROCESS BIAS

SYSTEM ARCHITECTURE WEB BASED SYSTEM • Windows 7 Professional 64-bit • Processor Intel ® Core ™ i5 • Installed Memory 2.048 MB • 280 GB hard disk space • 12.1” monitor • PHP Excel Reader library • JPGraph 3.5.0 library • PHP version 5.3.1

EXPERIMENTAL Initial values Learning rate Error tolerance v01 v11 v21 v31 w01 w11

: 0.1 : 0.05 : -1 : 1.5 :0 : 2.5 : -5 :7

50 JKSE composite index data taken weekly from 13 August 2012 to 29 July 2013.

EXPERIMENTAL

EXPERIMENTAL RESULT

n = 25

EXPERIMENTAL RESULT

n=5

EXPERIMENTAL RESULT

CONCLUSIONS • Backpropagation neural networks method using multi-layer networks can be used to forecast the JKSE composite index data. • Market traders can use the proposed method to take the best decision on buying or selling their shares based on the condition of the stock market.

FUTURE WORK • Building a new approach or algorithm to decide the best initial weights and bias values, so we can get a better forecasting result.

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