Comparative Study between FPA, BA, MCS, ABC, and PSO Algorithms in Training and Optimizing of LS-SVM for Stock Market Prediction
作者: Osman Hegazy Omar S. Soliman and Mustafa Abdul Salam
刊名: International Journal of Advanced Computer Research (IJACR), 2015, Vol.5 (18)
来源数据库: ACCENTS
关键词: Least Square- Support Vector MachineFlower Pollination AlgorithmBat algorithmModified Cuckoo SearchArtificial Bee ColonyParticle Swarm Optimizationand stock market prediction.
原始语种摘要: In this Paper, five recent natural inspired algorithms are proposed to optimize and train Least Square- Support Vector Machine (LS-SVM). These algorithms are namely, Flower Pollination Algorithm (FPA), Bat algorithm (BA), Modified Cuckoo Search (MCS), Artificial Bee Colony (ABC), and Particle Swarm Optimization (PSO). These algorithms are proposed to automatically select best free parameters combination for LS-SVM. Six financial technical indicators derived from stock historical data are used as inputs to proposed models. Standard LS-SVM and ANN are used as benchmarks for comparison with proposed models. Proposed models tested with six datasets representing different sectors in S&P 500 stock market. Proposed models were used to predict daily, weekly, and monthly stock prices. Results...
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关键词翻译
关键词翻译
  • Magnetic Core Storage BAttery
  • ABC ABC automatic coding system
  • BA BAttery
  • MCS BAttery
  • prices 行情
  • stock 岩株
  • proposed 建议的
  • SVM 共享虚拟存储器
  • between 在中间
  • market 市场
  • technical 技术的