Learning DAGs and Root Causes from Time-Series Data
We introduce DAG-TFRC, a novel method for learning directed acyclic graphs (DAGs) from time series with few root causes. By this, we mean that the data are generated by a small number of events at certain, unknown nodes and time points under a structural vector autoregression model. For such data, we (i) learn the DAGs representing both the instantaneous and time-lagged dependencies between nodes, and (ii) discover the location and time of the root causes. For synthetic data with few root causes, DAG-TFRC shows superior performance in accuracy and runtime over prior work, scaling up to thousands of nodes. Experiments on simulated and real-world financial data demonstrate the viability of our sparse root cause assumption. On S&P 500 data, DAG-TFRC successfully clusters stocks by sectors and discovers major stock movements as root causes.
Code (0)
등록된 구현이 없습니다.
Tasks
Time SeriesSimilar Papers 제목 키워드 기반
Learning DAGs from Data with Few Root Causes
We present a novel perspective and algorithm for learning directed acyclic graphs (DAGs) from data generated by a linear structural equation model (SEM). First, we show that a linear SEM can be viewed as a linear transfo…
A Causal Approach to Detecting Multivariate Time-series Anomalies and Root Causes
Detecting anomalies and the corresponding root causes in multivariate time series plays an important role in monitoring the behaviors of various real-world systems, e.g., IT system operations or manufacturing industry. P…
Anomaly DetectionTime SeriesTime Series AnalysisTime Series Anomaly DetectionRoot Cause Identification for Collective Anomalies in Time Series given an Acyclic Summary Causal Graph with Loops
This paper presents an approach for identifying the root causes of collective anomalies given observational time series and an acyclic summary causal graph which depicts an abstraction of causal relations present in a dy…
Time SeriesTime Series AnalysisScaling Causal Mediation for Complex Systems: A Framework for Root Cause Analysis
Modern operational systems ranging from logistics and cloud infrastructure to industrial IoT, are governed by complex, interdependent processes. Understanding how interventions propagate through such systems requires cau…
Causal InferenceRoot cause analysis via difference graph discovery from linear time-series data
Root cause analysis aims to identify the mechanisms responsible for anomalies in complex dynamical systems. In this paper, we study root cause analysis in linear time-series through the lens of difference graph discovery…