Multivariable time series processing method, device and medium

An electronic device, comprising:

• a memory for storing instructions or a computer program;
• and
• a processor for executing the instructions or the computer program in the memory to cause the electronic device to perform a method comprising:
• obtaining a time series set comprising a plurality of first time series segments, each of the plurality of first time series segments having a same length and being a multivariable time series;
• for each of the first time series segments in the time series set, inputting the first time series segment into a graph neural network to predict a multivariable reference value corresponding to a first time point that is a next time point adjacent to a latest time point in the first time series segment;
• determining an optimization function based on multivariable reference values corresponding to a plurality of the first time points and multivariable series tags corresponding to the plurality of the first time points, the optimization function comprising a loss function and a causal matrix of the graph neural network;
• determining values of respective parameters in the causal matrix with an objective of minimizing the optimization function;
• and
• determining, based on the values of the respective parameters in the causal matrix, a causal relationship between multiple variables in the multivariable time series.

A non-transitory computer readable storage medium having instructions stored thereon which, when running on a device, cause the device to perform a method comprising:

• obtaining a time series set comprising a plurality of first time series segments, each of the plurality of first time series segments having a same length and being a multivariable time series;
• for each of the first time series segments in the time series set, inputting the first time series segment into a graph neural network to predict a multivariable reference value corresponding to a first time point that is a next time point adjacent to a latest time point in the first time series segment;
• determining an optimization function based on multivariable reference values corresponding to a plurality of the first time points and multivariable series tags corresponding to the plurality of the first time points, the optimization function comprising a loss function and a causal matrix of the graph neural network;
• determining values of respective parameters in the causal matrix with an objective of minimizing the optimization function;
• and
• determining, based on the values of the respective parameters in the causal matrix, a causal relationship between multiple variables in the multivariable time series.