Temporal Scale Estimation for Oversampled Network Cascades: Theory, Algorithms, and Experiment

• URL: http://arxiv.org/abs/2109.10937v1
• Date: Wed, 22 Sep 2021 18:03:28 GMT
• Title: Temporal Scale Estimation for Oversampled Network Cascades: Theory, Algorithms, and Experiment
• Authors: Abram Magner and Carolyn Kaminski and Petko Bogdanov
• Abstract summary: Various discrete-time probabilistic models for spreading processes have been proposed. FastClock is a clock estimation algorithm that runs in linear time in the size of its input. We give empirical results on the performance of our algorithm in comparison to the state of the art estimator.
• Score: 8.43300003915341
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spreading processes on graphs arise in a host of application domains, from the study of online social networks to viral marketing to epidemiology. Various discrete-time probabilistic models for spreading processes have been proposed. These are used for downstream statistical estimation and prediction problems, often involving messages or other information that is transmitted along with infections caused by the process. It is thus important to design models of cascade observation that take into account phenomena that lead to uncertainty about the process state at any given time. We highlight one such phenomenon -- temporal distortion -- caused by a misalignment between the rate at which observations of a cascade process are made and the rate at which the process itself operates, and argue that failure to correct for it results in degradation of performance on downstream statistical tasks. To address these issues, we formulate the clock estimation problem in terms of a natural distortion measure. We give a clock estimation algorithm, which we call FastClock, that runs in linear time in the size of its input and is provably statistically accurate for a broad range of model parameters when cascades are generated from the independent cascade process with known parameters and when the underlying graph is Erd\H{o}s-R\'enyi. We further give empirical results on the performance of our algorithm in comparison to the state of the art estimator, a likelihood proxy maximization-based estimator implemented via dynamic programming. We find that, in a broad parameter regime, our algorithm substantially outperforms the dynamic programming algorithm in terms of both running time and accuracy.

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)
• Deep Ensembles Meets Quantile Regression: Uncertainty-aware Imputation for Time Series [45.76310830281876]
  We propose Quantile Sub-Ensembles, a novel method to estimate uncertainty with ensemble of quantile-regression-based task networks. Our method not only produces accurate imputations that is robust to high missing rates, but also is computationally efficient due to the fast training of its non-generative model.
  arXiv  Detail & Related papers  (2023-12-03T05:52:30Z)
• Online estimation methods for irregular autoregressive models [0.0]
  Currently available methods for addressing this problem, the so-called online learning methods, use current parameter estimations and novel data to update the estimators. In this work we consider three online learning algorithms for parameters estimation in the context of time series models.
  arXiv  Detail & Related papers  (2023-01-31T19:52:04Z)
• Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting [22.434845478979604]
  We propose a mechanism for mining causal structures from continuous-time models. We train models to capture the causal structure by enforcing sparsity in the weights of the input layers of the dynamics models. We apply our method to a real-world problem, namely tsunami forecasting, where the exact causal-structures are difficult to characterize.
  arXiv  Detail & Related papers  (2022-10-10T18:53:13Z)
• Online Time Series Anomaly Detection with State Space Gaussian Processes [12.483273106706623]
  R-ssGPFA is an unsupervised online anomaly detection model for uni- and multivariate time series. For high-dimensional time series, we propose an extension of Gaussian process factor analysis to identify the common latent processes of the time series. Our model's robustness is improved by using a simple to skip Kalman updates when encountering anomalous observations.
  arXiv  Detail & Related papers  (2022-01-18T06:43:32Z)
• Scalable Intervention Target Estimation in Linear Models [52.60799340056917]
  Current approaches to causal structure learning either work with known intervention targets or use hypothesis testing to discover the unknown intervention targets. This paper proposes a scalable and efficient algorithm that consistently identifies all intervention targets. The proposed algorithm can be used to also update a given observational Markov equivalence class into the interventional Markov equivalence class.
  arXiv  Detail & Related papers  (2021-11-15T03:16:56Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Quantifying Uncertainty in Deep Spatiotemporal Forecasting [67.77102283276409]
  We describe two types of forecasting problems: regular grid-based and graph-based. We analyze UQ methods from both the Bayesian and the frequentist point view, casting in a unified framework via statistical decision theory. Through extensive experiments on real-world road network traffic, epidemics, and air quality forecasting tasks, we reveal the statistical computational trade-offs for different UQ methods.
  arXiv  Detail & Related papers  (2021-05-25T14:35:46Z)
• Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting [2.8712862578745018]
  Updating observations of a signal due to the delays in the measurement process is a common problem in signal processing. We present a flexible approach using a latent Gaussian process that is capable of describing the changing auto-correlation structure present in the reporting time-delay surface. This approach also yields robust estimates of uncertainty for the estimated nowcasted numbers of deaths.
  arXiv  Detail & Related papers  (2021-02-22T18:32:44Z)
• Instability, Computational Efficiency and Statistical Accuracy [101.32305022521024]
  We develop a framework that yields statistical accuracy based on interplay between the deterministic convergence rate of the algorithm at the population level, and its degree of (instability) when applied to an empirical object based on $n$ samples. We provide applications of our general results to several concrete classes of models, including Gaussian mixture estimation, non-linear regression models, and informative non-response models.
  arXiv  Detail & Related papers  (2020-05-22T22:30:52Z)

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.