Causal Temporal Regime Structure Learning

• URL: http://arxiv.org/abs/2311.01412v3
• Date: Wed, 19 Feb 2025 17:09:47 GMT
• Title: Causal Temporal Regime Structure Learning
• Authors: Abdellah Rahmani, Pascal Frossard,
• Abstract summary: We present CASTOR, a novel method that concurrently learns the Directed Acyclic Graph (DAG) for each regime.<n>We establish the identifiability of the regimes and DAGs within our framework.<n>Experiments show that CASTOR consistently outperforms existing causal discovery models.
• Score: 49.77103348208835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding causal relationships in multivariate time series is essential for predicting and controlling dynamic systems in fields like economics, neuroscience, and climate science. However, existing causal discovery methods often assume stationarity, limiting their effectiveness when time series consist of sequential regimes, consecutive temporal segments with unknown boundaries and changing causal structures. In this work, we firstly introduce a framework to describe and model such time series. Then, we present CASTOR, a novel method that concurrently learns the Directed Acyclic Graph (DAG) for each regime while determining the number of regimes and their sequential arrangement. CASTOR optimizes the data log-likelihood using an expectation-maximization algorithm, alternating between assigning regime indices (expectation step) and inferring causal relationships in each regime (maximization step). We establish the identifiability of the regimes and DAGs within our framework. Extensive experiments show that CASTOR consistently outperforms existing causal discovery models in detecting different regimes and learning their DAGs across various settings, including linear and nonlinear causal relationships, on both synthetic and real world datasets.

Related papers

• Efficient Differentiable Discovery of Causal Order [14.980926991441342]
  Intersort is a score-based method to discover causal order of variables. We reformulate Intersort using differentiable sorting and ranking techniques. Our work opens the door to efficiently incorporating regularization for causal order into the training of differentiable models.
  arXiv  Detail & Related papers  (2024-10-11T13:11:55Z)
• RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms [0.393259574660092]
  RHiOTS is designed to assess the robustness of hierarchical time series forecasting models and algorithms on real-world datasets. RHiOTS incorporates an innovative visualization component, turning complex, multidimensional robustness evaluation results into intuitive, easily interpretable visuals. Our findings show that traditional statistical methods are more robust than state-of-the-art deep learning algorithms, except when the transformation effect is highly disruptive.
  arXiv  Detail & Related papers  (2024-08-06T18:52:15Z)
• TS-CausalNN: Learning Temporal Causal Relations from Non-linear Non-stationary Time Series Data [0.42156176975445486]
  We propose a Time-Series Causal Neural Network (TS-CausalNN) to discover contemporaneous and lagged causal relations simultaneously. In addition to the simple parallel design, an advantage of the proposed model is that it naturally handles the non-stationarity and non-linearity of the data.
  arXiv  Detail & Related papers  (2024-04-01T20:33:29Z)
• Structural Knowledge Informed Continual Multivariate Time Series Forecasting [23.18105409644709]
  We propose a novel Structural Knowledge Informed Continual Learning (SKI-CL) framework to perform MTS forecasting within a continual learning paradigm. Specifically, we develop a forecasting model based on graph structure learning, where a consistency regularization scheme is imposed between the learned variable dependencies and the structural knowledge. We develop a representation-matching memory replay scheme that maximizes the temporal coverage of MTS data to efficiently preserve the underlying temporal dynamics and dependency structures of each regime.
  arXiv  Detail & Related papers  (2024-02-20T05:11:20Z)
• Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling [14.170879566023098]
  We introduce a learnable decomposition strategy to capture dynamic trend information more reasonably. We also propose a dual attention module tailored to capture inter-series dependencies and intra-series variations simultaneously.
  arXiv  Detail & Related papers  (2024-02-20T03:45:59Z)
• Attractor Memory for Long-Term Time Series Forecasting: A Chaos Perspective [63.60312929416228]
  textbftextitAttraos incorporates chaos theory into long-term time series forecasting. We show that Attraos outperforms various LTSF methods on mainstream datasets and chaotic datasets with only one-twelfth of the parameters compared to PatchTST.
  arXiv  Detail & Related papers  (2024-02-18T05:35:01Z)
• Hierarchical Joint Graph Learning and Multivariate Time Series Forecasting [0.16492989697868887]
  We introduce a method of representing multivariate signals as nodes in a graph with edges indicating interdependency between them. We leverage graph neural networks (GNN) and attention mechanisms to efficiently learn the underlying relationships within the time series data. The effectiveness of our proposed model is evaluated across various real-world benchmark datasets designed for long-term forecasting tasks.
  arXiv  Detail & Related papers  (2023-11-21T14:24:21Z)
• Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models [61.10851158749843]
  Key insights can be obtained by discovering lead-lag relationships inherent in the data. We develop a clustering-driven methodology for robust detection of lead-lag relationships in lagged multi-factor models.
  arXiv  Detail & Related papers  (2023-05-11T10:30:35Z)
• Deep Explicit Duration Switching Models for Time Series [84.33678003781908]
  We propose a flexible model that is capable of identifying both state- and time-dependent switching dynamics. State-dependent switching is enabled by a recurrent state-to-switch connection. An explicit duration count variable is used to improve the time-dependent switching behavior.
  arXiv  Detail & Related papers  (2021-10-26T17:35:21Z)
• Path Signature Area-Based Causal Discovery in Coupled Time Series [0.0]
  We propose the application of confidence sequences to analyze the significance of the magnitude of the signed area between two variables. This approach provides a new way to define the confidence of a causal link existing between two time series.
  arXiv  Detail & Related papers  (2021-10-23T19:57:22Z)
• Modeling Regime Shifts in Multiple Time Series [1.4588552933974936]
  Regime shifts refer to the changing behaviors exhibited by series at different time intervals. Existing methods fail to take relationships between time series into consideration for discovering regimes in multiple time series. Most of the existing methods are unable to handle all of these three issues in a unified framework.
  arXiv  Detail & Related papers  (2021-09-20T17:02:29Z)
• Deep Switching State Space Model (DS$^3$M) for Nonlinear Time Series Forecasting with Regime Switching [2.8579459256051316]
  We introduce a novel modeling framework known as the Deep Switching State Space Model (DS$3$M) This framework is engineered to make accurate forecasts for such time series while adeptly identifying the irregular regimes hidden within the dynamics. We validate the effectiveness and regime identification capabilities of DS$3$M through short- and long-term forecasting tests on a wide array of simulated and real-world datasets.
  arXiv  Detail & Related papers  (2021-06-04T08:25:47Z)
• Consistency of mechanistic causal discovery in continuous-time using Neural ODEs [85.7910042199734]
  We consider causal discovery in continuous-time for the study of dynamical systems. We propose a causal discovery algorithm based on penalized Neural ODEs.
  arXiv  Detail & Related papers  (2021-05-06T08:48:02Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)
• Supporting Optimal Phase Space Reconstructions Using Neural Network Architecture for Time Series Modeling [68.8204255655161]
  We propose an artificial neural network with a mechanism to implicitly learn the phase spaces properties. Our approach is either as competitive as or better than most state-of-the-art strategies.
  arXiv  Detail & Related papers  (2020-06-19T21:04:47Z)
• Learned Factor Graphs for Inference from Stationary Time Sequences [107.63351413549992]
  We propose a framework that combines model-based algorithms and data-driven ML tools for stationary time sequences. neural networks are developed to separately learn specific components of a factor graph describing the distribution of the time sequence. We present an inference algorithm based on learned stationary factor graphs, which learns to implement the sum-product scheme from labeled data.
  arXiv  Detail & Related papers  (2020-06-05T07:06:19Z)
• Variational Hyper RNN for Sequence Modeling [69.0659591456772]
  We propose a novel probabilistic sequence model that excels at capturing high variability in time series data. Our method uses temporal latent variables to capture information about the underlying data pattern. The efficacy of the proposed method is demonstrated on a range of synthetic and real-world sequential data.
  arXiv  Detail & Related papers  (2020-02-24T19:30:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.