CausalTime: Realistically Generated Time-series for Benchmarking of
  Causal Discovery
Yuxiao Cheng Ziqian Wang Tingxiong Xiao Qin Zhong Jinli Suo Kunlun He
Time-series causal discovery (TSCD) is a fundamental problem of machine
learning. However, existing synthetic datasets cannot properly evaluate or
predict the algorithms' performance on real data. This study introduces the
CausalTime pipeline to generate time-series that highly resemble the real data
and with ground truth causal graphs for quantitative performance evaluation.
The pipeline starts from real observations in a specific scenario and produces
a matching benchmark dataset. Firstly, we harness deep neural networks along
with normalizing flow to accurately capture realistic dynamics. Secondly, we
extract hypothesized causal graphs by performing importance analysis on the
neural network or leveraging prior knowledge. Thirdly, we derive the ground
truth causal graphs by splitting the causal model into causal term, residual
term, and noise term. Lastly, using the fitted network and the derived causal
graph, we generate corresponding versatile time-series proper for algorithm
assessment. In the experiments, we validate the fidelity of the generated data
through qualitative and quantitative experiments, followed by a benchmarking of
existing TSCD algorithms using these generated datasets. CausalTime offers a
feasible solution to evaluating TSCD algorithms in real applications and can be
generalized to a wide range of fields. For easy use of the proposed approach,
we also provide a user-friendly website, hosted on www.causaltime.cc.