Neural Network Revisited: Perception on Modified Poincare Map of Financial Time Series Data

TL;DR

This paper explores using a Multi-Layer Perceptron to analyze and predict Indonesian stock exchange data by transforming it into a modified Poincare map, offering a novel approach to financial time-series prediction.

Contribution

It introduces a new method combining modified Poincare maps with neural networks for financial data analysis, specifically applied to Indonesian stock exchange data.

Findings

MLP successfully predicts stock data patterns.

Modified Poincare map enhances data pattern recognition.

Method shows promise for financial forecasting.

Abstract

Artificial Neural Network Model for prediction of time-series data is revisited on analysis of the Indonesian stock-exchange data. We introduce the use of Multi-Layer Perceptron to percept the modified Poincare-map of the given financial time-series data. The modified Poincare-map is believed to become the pattern of the data that transforms the data in time-t versus the data in time-t+1 graphically. We built the Multi-Layer Perceptron to percept and demonstrate predicting the data on specific stock-exchange in Indonesia.