英文标题：
《Using String Invariants for Prediction Searching for Optimal Parameters》
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作者：
Marek Bundzel, Tomas Kasanicky, Richard Pincak
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最新提交年份：
2016
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英文摘要：
  We have developed a novel prediction method based on string invariants. The method does not require learning but a small set of parameters must be set to achieve optimal performance. We have implemented an evolutionary algorithm for the parametric optimization. We have tested the performance of the method on artificial and real world data and compared the performance to statistical methods and to a number of artificial intelligence methods. We have used data and the results of a prediction competition as a benchmark. The results show that the method performs well in single step prediction but the methods performance for multiple step prediction needs to be improved. The method works well for a wide range of parameters.
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中文摘要：
我们开发了一种基于字符串不变量的新预测方法。该方法不需要学习，但必须设置一小组参数以实现最佳性能。我们实现了一种用于参数优化的进化算法。我们在人工和真实数据上测试了该方法的性能，并将其与统计方法和一些人工智能方法进行了比较。我们使用数据和预测比赛的结果作为基准。结果表明，该方法在单步预测中表现良好，但在多步预测中的性能有待提高。该方法适用于范围广泛的参数。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Quantitative Finance        数量金融学
二级分类：Computational Finance        计算金融学
分类描述：Computational methods, including Monte Carlo, PDE, lattice and other numerical methods with applications to financial modeling
计算方法，包括蒙特卡罗，偏微分方程，格子和其他数值方法，并应用于金融建模
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一级分类：Quantitative Finance        数量金融学
二级分类：Mathematical Finance        数学金融学
分类描述：Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法，包括随机、概率和泛函分析、代数、几何和其他方法
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