# Statistical and Machine Learning approach in forex prediction based on empirical data
> Sidehabi S.

URL kanonis: https://discover.unhas.ac.id/publications/pub_scopus_85018949507
Jurnal / Konferensi: Proceedings Cyberneticscom 2016 International Conference on Computational Intelligence and Cybernetics
Tahun terbit: 2017
DOI: https://doi.org/10.1109/CyberneticsCom.2016.7892568
Citations: 15

## Authors
- Sidehabi S.

## Abstract
This study proposed a new insight in comparing common methods used in predicting based on data series i.e statistical method and machine learning. The corresponding techniques are use in predicting Forex (Foreign Exchange) rates. The Statistical method used in this paper is Adaptive Spline Threshold Autoregression (ASTAR), while for machine learning, Support Vector Machine (SVM) and hybrid form of Genetic Algorithm-Neural Network (GA-NN) are chosen. The comparison among the three methods accurate rate is measured in root mean squared error (RMSE). It is found that ASTAR and GA-NN method has advantages depend on the period time intervals.

## Keywords
- Support vector machine
- Mean squared error
- Computer science
- Artificial intelligence
- Artificial neural network
- Foreign exchange market
- Machine learning
- Time series
- Autoregressive integrated moving average
- Vector autoregression
- Pattern recognition (psychology)
- Exchange rate
- Mathematics
- Statistics
- Economics
- Macroeconomics

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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