Binomial distribution based approach to deriving time series weights

This paper introduces a time series weighted aggregation (TSWA) operator, and proposes an approach based on the binomial distribution probability density function and its inverse form to deriving the time series weights associated with the TSWA operator.

Abstract

Information aggregation is an essential process in many fields like mathematics, economy, biology, engineering, management, medicine and military affairs, etc. A variety of techniques have been developed to aggregate argument information collected at the same period. However, it seems that there is little investigation on the aggregation process of arguments collected at different periods, in which time series weights play an important role and thus should be highly emphasized. In this paper, we introduce a time series weighted aggregation (TSWA) operator, and propose an approach based on the binomial distribution probability density function and its inverse form to deriving the time series weights associated with the TSWA operator. Then, we give a detailed analysis of the properties of the derived time series weights.