Stock Prediction Based on Phase Space Reconstruction and Echo State Networks

In this paper a synthetic model for stock prediction is proposed based on phase space reconstruction, Echo State Networks (ESN) and Moving Average Convergence/Divergence (MACD). In this model, time series data is reconstructed in phase space before feeding to the ESN. Guided by the MACD strategy, st...

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Main Authors:	Huaguang Zhang, Jiuzhen Liang, Zhilei Chai
Format:	Article
Language:	English
Published:	SAGE Publishing 2013-03-01
Series:	Journal of Algorithms & Computational Technology
Online Access:	https://doi.org/10.1260/1748-3018.7.1.87

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spelling	doaj-7b36175d2b8840f29a55bfe785ceb1d02020-11-25T03:20:53ZengSAGE PublishingJournal of Algorithms & Computational Technology1748-30181748-30262013-03-01710.1260/1748-3018.7.1.87Stock Prediction Based on Phase Space Reconstruction and Echo State NetworksHuaguang ZhangJiuzhen LiangZhilei ChaiIn this paper a synthetic model for stock prediction is proposed based on phase space reconstruction, Echo State Networks (ESN) and Moving Average Convergence/Divergence (MACD). In this model, time series data is reconstructed in phase space before feeding to the ESN. Guided by the MACD strategy, stock prices are predicted and valuable trading advices are provided. The proposed model is tested by Microsoft Company stock price. The experiments are compared with those provided by other models such as BackPropagation Neural Networks (BNN), Support Vector Machine (SVM), Hidden Markov Model (HMM) and Procedural Neural Networks (PNN). The test results show that the proposed model is effective and efficient in stock price prediction.https://doi.org/10.1260/1748-3018.7.1.87
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Huaguang Zhang Jiuzhen Liang Zhilei Chai
spellingShingle	Huaguang Zhang Jiuzhen Liang Zhilei Chai Stock Prediction Based on Phase Space Reconstruction and Echo State Networks Journal of Algorithms & Computational Technology
author_facet	Huaguang Zhang Jiuzhen Liang Zhilei Chai
author_sort	Huaguang Zhang
title	Stock Prediction Based on Phase Space Reconstruction and Echo State Networks
title_short	Stock Prediction Based on Phase Space Reconstruction and Echo State Networks
title_full	Stock Prediction Based on Phase Space Reconstruction and Echo State Networks
title_fullStr	Stock Prediction Based on Phase Space Reconstruction and Echo State Networks
title_full_unstemmed	Stock Prediction Based on Phase Space Reconstruction and Echo State Networks
title_sort	stock prediction based on phase space reconstruction and echo state networks
publisher	SAGE Publishing
series	Journal of Algorithms & Computational Technology
issn	1748-3018 1748-3026
publishDate	2013-03-01
description	In this paper a synthetic model for stock prediction is proposed based on phase space reconstruction, Echo State Networks (ESN) and Moving Average Convergence/Divergence (MACD). In this model, time series data is reconstructed in phase space before feeding to the ESN. Guided by the MACD strategy, stock prices are predicted and valuable trading advices are provided. The proposed model is tested by Microsoft Company stock price. The experiments are compared with those provided by other models such as BackPropagation Neural Networks (BNN), Support Vector Machine (SVM), Hidden Markov Model (HMM) and Procedural Neural Networks (PNN). The test results show that the proposed model is effective and efficient in stock price prediction.
url	https://doi.org/10.1260/1748-3018.7.1.87
work_keys_str_mv	AT huaguangzhang stockpredictionbasedonphasespacereconstructionandechostatenetworks AT jiuzhenliang stockpredictionbasedonphasespacereconstructionandechostatenetworks AT zhileichai stockpredictionbasedonphasespacereconstructionandechostatenetworks
_version_	1724615990088564736

Stock Prediction Based on Phase Space Reconstruction and Echo State Networks

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