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Twin support vector regression (TSVR) is a useful extension of traditional support vector regression (SVR). As a new regression model, the basic idea of TSVR is generating a pair of nonparallel functions on both sides of the training data points, such that the ε -insensitive upper and lower bounds of the regression function can be determined. Owing to its excellent learning ability, TSVR has become a research hotspot in the field of machine learning. With the deepening of such research, scholars have found that TSVR also has certain limitations, and thus various improved models have been proposed. This review aims to report the recent developments in twin support vector regression. First, the basic concepts and basic models of TSVR are introduced. Second, the improved algorithms and applications of TSVR in recent years are summarized, and the advantages and disadvantages of its representative algorithms are analyzed and compared with the experiments. Finally, we discuss the research conducted on TSVR. [ABSTRACT FROM AUTHOR]