Shalev Yuval, Ben-Gal Irad
Laboratory for AI, Machine Learning, Business & Data Analytics, Department of Industrial Engineering, The Tel-Aviv University, Ramat-Aviv 6997801, Israel.
Entropy (Basel). 2019 Jun 29;21(7):645. doi: 10.3390/e21070645.
We propose a new algorithm called the context-based predictive information (CBPI) for estimating the predictive information (PI) between time series, by utilizing a lossy compression algorithm. The advantage of this approach over existing methods resides in the case of sparse predictive information (SPI) conditions, where the ratio between the number of informative sequences to uninformative sequences is small. It is shown that the CBPI achieves a better PI estimation than benchmark methods by ignoring uninformative sequences while improving explainability by identifying the informative sequences. We also provide an implementation of the CBPI algorithm on a real dataset of large banks' stock prices in the U.S. In the last part of this paper, we show how the CBPI algorithm is related to the well-known information bottleneck in its deterministic version.
我们提出了一种名为基于上下文的预测信息（CBPI）的新算法，用于通过利用有损压缩算法来估计时间序列之间的预测信息（PI）。这种方法相对于现有方法的优势在于稀疏预测信息（SPI）条件的情况，即在信息序列数量与非信息序列数量之比很小的情况下。结果表明，CBPI通过忽略非信息序列实现了比基准方法更好的PI估计，同时通过识别信息序列提高了可解释性。我们还在美国大型银行股价的真实数据集上提供了CBPI算法的实现。在本文的最后部分，我们展示了CBPI算法在其确定性版本中与著名的信息瓶颈是如何相关的。
