A Long Memory Pattern Modelling and Recognition System for Financial Time-Series Forecasting

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• 作者：S. Singh
• 关键词：Key words ; Financial forecasting ; Financial indices ; Neural networks ; Pattern recognition ; PMRS
• 刊名：Pattern Analysis & Applications
• 出版年：1999
• 出版时间：August 1999
• 年：1999
• 卷：2
• 期：3
• 页码：264-273
• 全文大小：220 KB
• 刊物类别：Computer Science
• 刊物主题：Pattern Recognition
  
• 出版者：Springer London
• ISSN：1433-755X

文摘

In this paper, the concept of a long memory system for forecasting is developed. Pattern modelling and recognition systems are introduced as local approximation tools for forecasting. Such systems are used for matching the current state of the time-series with past states to make a forecast. In the past, this system has been successfully used for forecasting the Santa Fe competition data. In this paper, we forecast the financial indices of six different countries, and compare the results with neural networks on five different error measures. The results show that pattern recognition-based approaches in time-series forecasting are highly accurate, and that these are able to match the performance of advanced methods such as neural networks.