Time series modeling and forecasting based on a Markov chain with changing transition matrices
作者：
Highlights：
• Markov models can be an effective way of prediction in time series.
• The discretization of the state space is of importance for the quality of prediction.
• Time windows grouped in sequences were used to obtain good transition matrices.
• Good results of profit according to the Calmar criterion were obtained.
• In order to optimize the current parameters of the method, machine learning was used.
摘要
•Markov models can be an effective way of prediction in time series.•The discretization of the state space is of importance for the quality of prediction.•Time windows grouped in sequences were used to obtain good transition matrices.•Good results of profit according to the Calmar criterion were obtained.•In order to optimize the current parameters of the method, machine learning was used.
论文关键词：Markov chain,Prediction in time series,Investment strategies,Machine learning,Transition matrices
论文评审过程：Received 20 December 2017, Revised 17 April 2019, Accepted 30 April 2019, Available online 14 May 2019, Version of Record 20 May 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.04.067
