Kozak Jan, Kania Krzysztof, Juszczuk Przemysław
Faculty of Informatics and Communication; Department of Knowledge Engineering, University of Economics, 1 Maja 50, 40-287 Katowice, Poland.
Entropy (Basel). 2020 Mar 13;22(3):330. doi: 10.3390/e22030330.
Financial markets give a large number of trading opportunities. However, over-complicated systems make it very difficult to be effectively used by decision-makers. Volatility and noise present in the markets evoke a need to simplify the market picture derived for the decision-makers. Symbolic representation fits in this concept and greatly reduces data complexity. However, at the same time, some information from the market is lost. Our motivation is to answer the question: What is the impact of introducing different data representation on the overall amount of information derived for the decision-maker? We concentrate on the possibility of using entropy as a measure of the information gain/loss for the financial data, and as a basic form, we assume permutation entropy with later modifications. We investigate different symbolic representations and compare them with classical data representation in terms of entropy. The real-world data covering the time span of 10 years are used in the experiments. The results and the statistical verification show that extending the symbolic description of the time series does not affect the permutation entropy values.
金融市场提供了大量的交易机会。然而，过于复杂的系统使得决策者很难有效地利用它们。市场中存在的波动性和噪声引发了简化为决策者得出的市场图景的需求。符号表示符合这一概念，并大大降低了数据复杂性。然而，与此同时，一些来自市场的信息丢失了。我们的动机是回答这个问题：引入不同的数据表示对为决策者得出的信息总量有什么影响？我们专注于使用熵作为金融数据信息增益/损失度量的可能性，并且作为基本形式，我们假设排列熵并随后进行修改。我们研究不同的符号表示，并在熵方面将它们与经典数据表示进行比较。实验中使用了涵盖10年时间跨度的真实世界数据。结果和统计验证表明，扩展时间序列的符号描述不会影响排列熵值。
