Alibaba Group Holding Limited
REGRESSION MODELING OF SPARSE ACYCLIC GRAPHS IN TIME SERIES CAUSAL INFERENCE

Abstract:

By the abovementioned technical solutions, the present disclosure provides optimizing a vector autoregressive model conforming to structural constraints of sparsity and acyclicity. A regularization term is introduced to the model to impose the sparsity structural constraint such that most off-diagonal coefficients of an autoregressive coefficient matrix are forced to zero values. One or more penalty terms are introduced to the model to impose the acyclicity structural constraint such that coefficients of the main diagonal are not causally self-related. The resulting model is then reformulated for computation as an augmented Lagrangian function, and further computed for different parameters in alternating iterations to make the computations tractable and within magnitude and precision limits of digital computers. Models of the present disclosure provide improved computing performance over existing models by directly inferring a sparse causal network having a directed acyclic graph structure without a separate pruning step.