Structural learning of neural networks for forecasting stock prices

Junzo Watada

    研究成果: Conference contribution

    抄録

    Generally, a neural network spends much computation time and cost in forecasting the value and movement of a stock. The reason is because a neural network requires exponential time in computation according to the number of units in a hidden layer. The objective of the paper is to optimally build a neural network through structurally learning. The results enable us to reduce the computational time and cost as well as to understand the structure more easily. In the paper the method is employed in forecasting the price movement of a stock. The optimization of the network by the structured learning is evaluated based on its real use.

    本文言語English
    ホスト出版物のタイトルLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    ページ972-979
    ページ数8
    4253 LNAI - III
    出版ステータスPublished - 2006
    イベント10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2006 - Bournemouth
    継続期間: 2006 10 92006 10 11

    出版物シリーズ

    名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    4253 LNAI - III
    ISSN(印刷版)03029743
    ISSN(電子版)16113349

    Other

    Other10th International Conference on Knowledge-Based Intelligent Information and Engineering Systems, KES 2006
    CityBournemouth
    Period06/10/906/10/11

    ASJC Scopus subject areas

    • コンピュータ サイエンス(全般)
    • 生化学、遺伝学、分子生物学(全般)
    • 理論的コンピュータサイエンス

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