Causal Discovery Algorithm for Non-stationary Time Series
To infer the causality of non-stationary time series accurately,a method of causal discovery algorithm based on piecewise ag-gregate variable-lag transfer entropy was proposed to overcome shortcomings of traditional methods of analyzing the causality of non-sta-tionary and variable-lag time series.Firstly,we used piecewise aggregate approximation to transform the time series and extract features.Then,dynamic time warping was used to infer variable-lag transfer entropy,which implemented causal relations between non-stationary time series.The effectiveness of the proposed method is verified by the results of simulations and comparison with existing methods.We successfully applied this method to the concentration of PM2.5 and meteorological time series in Changping,Beijing,which shows that the algorithm is widely applicable.
non-stationary time seriespiecewise aggregate approximationtransfer entropyvariable-lagcausal relations
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