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Soft Computing

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01.05.2013 | Methodologies and Application

Financial time series forecasting using LPP and SVM optimized by PSO

verfasst von: Guo Zhiqiang, Wang Huaiqing, Liu Quan

Erschienen in: Soft Computing | Ausgabe 5/2013

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Abstract

In this paper, a predicting model is constructed to forecast stock market behavior with the aid of locality preserving projection, particle swarm optimization, and a support vector machine. First, four stock market technique variables are selected as the input feature, and a slide window is used to obtain the input raw data of the model. Second, the locality preserving projection method is utilized to reduce the dimension of the raw data and to extract the intrinsic feature to improve the performance of the predicting model. Finally, a support vector machine optimized using particle swarm optimization is applied to forecast the next day’s price movement. The proposed model is used with the Shanghai stock market index and the Dow Jones index, and experimental results show that the proposed model performs better than other models in the areas of prediction accuracy rate and profit.

Vorheriger Artikel Large data sets classification using convex–concave hull and support vector machine

Nächster Artikel Neighborhood field for cooperative optimization

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Titel: Financial time series forecasting using LPP and SVM optimized by PSO
verfasst von: Guo Zhiqiang
Wang Huaiqing
Liu Quan
Publikationsdatum: 01.05.2013
Verlag: Springer-Verlag
Erschienen in: Soft Computing / Ausgabe 5/2013
Print ISSN: 1432-7643
Elektronische ISSN: 1433-7479
DOI: https://doi.org/10.1007/s00500-012-0953-y

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