TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data

arXiv:2404.01466v1 Announce Type: new
Abstract: The growing availability and importance of time series data across various domains, including environmental science, epidemiology, and economics, has led to an increasing need for time-series causal discovery methods that can identify the intricate relationships in the non-stationary, non-linear, and often noisy real world data. However, the majority of current time series causal discovery methods assume stationarity and linear relations in data, making them infeasible for the task. Further, the recent deep learning-based methods rely …

abstract arxiv availability causal cs.lg data discovery domains economics environmental environmental science epidemiology identify importance linear non-linear relations relationships science series stat.me temporal time series type world