Markov property checking methods for time series data

In applicable scenarios, data used for forecasting and decision-making is usually expected to exhibit characteristics like time stationarity and the Markov property, and etc. However, industrial applications often skip verifying whether the data meets these requirements, aiming to save time and effort, which may lead to inaccurate results. This paper explores the Markov property checking method from statistical and information-theoretic perspectives, and utilizes two types of time series data, named AAPL stock prices data and BPIC2012 event logs, respectively, to validate the effectiveness of proposed checking method. Experimental results show that datasets that conforming to the Markov property tend to perform better in predictive tasks.