Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis

• URL: http://arxiv.org/abs/2002.00208v3
• Date: Mon, 1 Jun 2020 09:24:52 GMT
• Title: Variable-lag Granger Causality and Transfer Entropy for Time Series Analysis
• Authors: Chainarong Amornbunchornvej, Elena Zheleva, and Tanya Berger-Wolf
• Abstract summary: We develop Variable-lag Granger causality and Variable-lag Transfer Entropy, which relax the assumption of the fixed time delay. In our approaches, we utilize an optimal warping path of Dynamic Time Warping (DTW) to infer variable-lag causal relations. Our approaches can be applied in any domain of time series analysis.
• Score: 7.627597166844701
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Granger causality is a fundamental technique for causal inference in time series data, commonly used in the social and biological sciences. Typical operationalizations of Granger causality make a strong assumption that every time point of the effect time series is influenced by a combination of other time series with a fixed time delay. The assumption of fixed time delay also exists in Transfer Entropy, which is considered to be a non-linear version of Granger causality. However, the assumption of the fixed time delay does not hold in many applications, such as collective behavior, financial markets, and many natural phenomena. To address this issue, we develop Variable-lag Granger causality and Variable-lag Transfer Entropy, generalizations of both Granger causality and Transfer Entropy that relax the assumption of the fixed time delay and allow causes to influence effects with arbitrary time delays. In addition, we propose methods for inferring both variable-lag Granger causality and Transfer Entropy relations. In our approaches, we utilize an optimal warping path of Dynamic Time Warping (DTW) to infer variable-lag causal relations. We demonstrate our approaches on an application for studying coordinated collective behavior and other real-world casual-inference datasets and show that our proposed approaches perform better than several existing methods in both simulated and real-world datasets. Our approaches can be applied in any domain of time series analysis. The software of this work is available in the R-CRAN package: VLTimeCausality.

Related papers

• Timer-XL: Long-Context Transformers for Unified Time Series Forecasting [67.83502953961505]
  We present Timer-XL, a generative Transformer for unified time series forecasting. Timer-XL achieves state-of-the-art performance across challenging forecasting benchmarks through a unified approach.
  arXiv  Detail & Related papers  (2024-10-07T07:27:39Z)
• Robust Predictions with Ambiguous Time Delays: A Bootstrap Strategy [5.71557730775514]
  Time Series Model Bootstrap (TSMB) is a versatile framework designed to handle potentially varying or even nondeterministic time delays in time series modeling. TSMB significantly bolsters the performance of models that are trained and make predictions using this framework, making it highly suitable for a wide range of dynamic and interconnected data environments.
  arXiv  Detail & Related papers  (2024-08-23T02:38:20Z)
• Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection [67.40407388422514]
  We design a conceptual fine-grained causal model named TBN Granger Causality. Second, we propose an end-to-end deep generative model called TacSas, which discovers TBN Granger Causality in a generative manner. We test TacSas on climate benchmark ERA5 for climate forecasting and the extreme weather benchmark of NOAA for extreme weather alerts.
  arXiv  Detail & Related papers  (2024-08-08T06:47:21Z)
• On the Identification of Temporally Causal Representation with Instantaneous Dependence [50.14432597910128]
  Temporally causal representation learning aims to identify the latent causal process from time series observations. Most methods require the assumption that the latent causal processes do not have instantaneous relations. We propose an textbfIDentification framework for instantanetextbfOus textbfLatent dynamics.
  arXiv  Detail & Related papers  (2024-05-24T08:08:05Z)
• Towards Understanding the Generalizability of Delayed Stochastic Gradient Descent [63.43247232708004]
  A gradient descent performed in an asynchronous manner plays a crucial role in training large-scale machine learning models. Existing generalization error bounds are rather pessimistic and cannot reveal the correlation between asynchronous delays and generalization. Our theoretical results indicate that asynchronous delays reduce the generalization error of the delayed SGD algorithm.
  arXiv  Detail & Related papers  (2023-08-18T10:00:27Z)
• Hawkes Processes with Delayed Granger Causality [9.664517084506718]
  We explicitly model the delayed Granger causal effects based on multivariate Hawkes processes. We infer the posterior distribution of the time lags and understand how this distribution varies across different scenarios. We empirically evaluate our model's event prediction and time-lag inference accuracy on synthetic and real data.
  arXiv  Detail & Related papers  (2023-08-11T12:43:43Z)
• 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)
• Generic Temporal Reasoning with Differential Analysis and Explanation [61.96034987217583]
  We introduce a novel task named TODAY that bridges the gap with temporal differential analysis. TODAY evaluates whether systems can correctly understand the effect of incremental changes. We show that TODAY's supervision style and explanation annotations can be used in joint learning.
  arXiv  Detail & Related papers  (2022-12-20T17:40:03Z)
• Jacobian Granger Causal Neural Networks for Analysis of Stationary and Nonstationary Data [19.347558051611827]
  We introduce a neural network-based approach to Granger causality using the Jacobian as a measure of variable importance. The resulting approach performs consistently well compared to other approaches in identifying Granger causal variables.
  arXiv  Detail & Related papers  (2022-05-19T14:07:54Z)
• Causal Inference in Non-linear Time-series using Deep Networks and Knockoff Counterfactuals [8.56007054019834]
  Non-linear coupling of variables is one of the major challenges inaccurate estimation of cause-effect relations. We propose to use deep autoregressive networks (DeepAR) in tandem with counterfactual analysis to infer nonlinear causal relations.
  arXiv  Detail & Related papers  (2021-09-22T16:07:27Z)
• Dynamic Window-level Granger Causality of Multi-channel Time Series [28.302491046127443]
  We present the dynamic window-level Granger causality method (DWGC) for multi-channel time series data. We propose the causality indexing trick in our DWGC method to reweight the original time series data.
  arXiv  Detail & Related papers  (2020-06-14T03:53:42Z)

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.