Work detail

Forex forecasting with Support vector regression and Long short-term memory recurrent neural network

Author: Bc. Michal Bodický
Year: 2021 - summer
Leaders: Mgr. Jan Šíla MSc.
Consultants:
Work type: Bachelors
Language: English
Pages: 56
Awards and prizes:
Link: https://ckis.cuni.cz:443/F/?func=direct&doc_number=002448217&local_base=CKS01&format=999
Abstract: In the last years, the field of machine learning boomed. That led to its
numerous forecasting applications on prices of Foreign exchange market. Researchers there mostly compare neural networks to linear model baselines.
The contribution of this thesis consists of a comprehensive performance comparison between two promising machine learning methods, Support vector
regression (SVR) and Long short-term memory recurrent neural network
(LSTM RNN), in the forecasting of six highly traded currency pairs on one
minute univariate time series data. First, it analyses methods’ performances
in the forecasting of one step ahead price while varying input dimensions of
these methods. Next, it examines how methods perform in longer forecasts,
that enabled by using a recurrent version of SVR. In the first analysis, LSTM
RNN outperforms SVR in most of the cases several times. Performance of
SVR is robust to varying input while LSTM RNN’s performance fluctuates
across dimensions. In the second analysis, LSTM RNN beats SVR mostly by
order of magnitude. With increasing forecasting horizon, SVR’s performance
gets worse and LSTM RNN’s performance remains stable.

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Deloitte

Sponsors

CRIF
McKinsey
Patria Finance