Partial least squares (PLS) is a multivariate statistical analytical method which can be used to extract valuable information from high-dimensional and correlated data effectively. To model the inevitable dynamics in industrial processes, dynamic extensions of PLS have been widely studied. Nowadays, however, because of the improvement of sensory technologies, redundant information exists among adjacent samples with high sampling rates. The existing dynamic algorithms ignore the redundant information, causing suboptimal modeling and monitoring schemes. In this paper, a dynamic weighted PLS (DWPLS) method is proposed to deal with the aforementioned issue. DWPLS maximizes the covariance between the quality latent scores and a weighted representation of lagged process scores. The relations of lagged process scores are learned through a weighted summation of basis functions. The monitoring scheme of DWPLS is also constructed. Case studies with two processes, the Tennessee Eastman process and a distillation process, are conducted to show the advantages of DWPLS over existing methods in terms of regression and fault detection performance.