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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 bydoi:10.3390/e21070645 pmid:33267359 pmcid:PMC7515138 fatcat:iafgwsciqbdwtdapxa7mno6tba