Abstract: Probabilistic causal relationships are implied in the data of various time slices in time-series databases.The fusion result of probabilistic causalities among multiple slices could provide with the reasonable guideline for decision-making,knowledge fusion,analysis,prediction of economic trends,etc.In this paper,we focus on the qualitative representation and fusion of probabilistic causalities on the same set of variables in multiple time slices.To represent the causalities efficiently and succinctly,we first construct QPNs abstracted from corresponding BNs in different time slices.Further,based on the reasoning approach of BNand the Markov assumption in time-series environments,we obtain the consistent representation of various network structures,and then propose the method for fusing the graphical structures of concerned QPNs.Consequently,we associate strengths to qualitative influences by interval values derived from BNs’ probability parameters.Then we give a superposition method for fusing qualitative parameters of time-series QPNs.Experimental results verify the feasibility of our methods.