Abstract:
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.
Similar content being viewed by others
Author information
Authors and Affiliations
Additional information
Received: 2 April 1998¶Received in revised form: 1 February 1999¶Accepted: 16 February 1999
Rights and permissions
About this article
Cite this article
Singh, S. A Long Memory Pattern Modelling and Recognition System for Financial Time-Series Forecasting. Pattern Analysis & Applications 2, 264–273 (1999). https://doi.org/10.1007/s100440050034
Issue Date:
DOI: https://doi.org/10.1007/s100440050034