Probabilistic Learning of Multivariate Time Series with Temporal Irregularity

• URL: http://arxiv.org/abs/2306.09147v2
• Date: Fri, 16 Jun 2023 05:56:20 GMT
• Title: Probabilistic Learning of Multivariate Time Series with Temporal Irregularity
• Authors: Yijun Li, Cheuk Hang Leung, Qi Wu
• Abstract summary: temporal irregularities, including nonuniform time intervals and component misalignment. We develop a conditional flow representation to non-parametrically represent the data distribution, which is typically non-Gaussian. The broad applicability and superiority of the proposed solution are confirmed by comparing it with existing approaches through ablation studies and testing on real-world datasets.
• Score: 25.91078012394032
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multivariate sequential data collected in practice often exhibit temporal irregularities, including nonuniform time intervals and component misalignment. However, if uneven spacing and asynchrony are endogenous characteristics of the data rather than a result of insufficient observation, the information content of these irregularities plays a defining role in characterizing the multivariate dependence structure. Existing approaches for probabilistic forecasting either overlook the resulting statistical heterogeneities, are susceptible to imputation biases, or impose parametric assumptions on the data distribution. This paper proposes an end-to-end solution that overcomes these limitations by allowing the observation arrival times to play the central role of model construction, which is at the core of temporal irregularities. To acknowledge temporal irregularities, we first enable unique hidden states for components so that the arrival times can dictate when, how, and which hidden states to update. We then develop a conditional flow representation to non-parametrically represent the data distribution, which is typically non-Gaussian, and supervise this representation by carefully factorizing the log-likelihood objective to select conditional information that facilitates capturing time variation and path dependency. The broad applicability and superiority of the proposed solution are confirmed by comparing it with existing approaches through ablation studies and testing on real-world datasets.

Related papers

• 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)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Temporally Disentangled Representation Learning under Unknown Nonstationarity [35.195001384417395]
  We introduce NCTRL, a principled estimation framework, to reconstruct time-delayed latent causal variables. Empirical evaluations demonstrated the reliable identification of time-delayed latent causal influences.
  arXiv  Detail & Related papers  (2023-10-28T06:46:03Z)
• Approximating Counterfactual Bounds while Fusing Observational, Biased and Randomised Data Sources [64.96984404868411]
  We address the problem of integrating data from multiple, possibly biased, observational and interventional studies. We show that the likelihood of the available data has no local maxima. We then show how the same approach can address the general case of multiple datasets.
  arXiv  Detail & Related papers  (2023-07-31T11:28:24Z)
• Continuous Time Evidential Distributions for Irregular Time Series [18.51671507076908]
  We present EDICT, a strategy that learns an evidential distribution over irregular time series in continuous time. This distribution enables well-calibrated and flexible inference of partially observed features at any time of interest. We demonstrate that EDICT attains competitive performance on challenging time series classification tasks.
  arXiv  Detail & Related papers  (2023-07-25T13:54:00Z)
• Maximum Likelihood With a Time Varying Parameter [0.0]
  We consider the problem of tracking an unknown time varying parameter that characterizes the evolution of a sequence of independent observations. We propose a gradient descent-based scheme in which the log-likelihood of the observations acts as time varying gain function.
  arXiv  Detail & Related papers  (2023-02-28T12:40:42Z)
• Causality-Based Multivariate Time Series Anomaly Detection [63.799474860969156]
  We formulate the anomaly detection problem from a causal perspective and view anomalies as instances that do not follow the regular causal mechanism to generate the multivariate data. We then propose a causality-based anomaly detection approach, which first learns the causal structure from data and then infers whether an instance is an anomaly relative to the local causal mechanism. We evaluate our approach with both simulated and public datasets as well as a case study on real-world AIOps applications.
  arXiv  Detail & Related papers  (2022-06-30T06:00:13Z)
• Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations [84.42837346400151]
  Estimating counterfactual outcomes over time has the potential to unlock personalized healthcare. Existing causal inference approaches consider regular, discrete-time intervals between observations and treatment decisions. We propose a controllable simulation environment based on a model of tumor growth for a range of scenarios.
  arXiv  Detail & Related papers  (2022-06-16T17:15:15Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• Independence Testing for Temporal Data [14.25244839642841]
  A fundamental question is whether two time-series are related or not. Existing approaches often have limitations, such as relying on parametric assumptions. This paper introduces the temporal dependence statistic with block permutation to test independence between temporal data.
  arXiv  Detail & Related papers  (2019-08-18T17:19:16Z)

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.