PyChEst: a Python package for the consistent retrospective estimation of distributional changes in piece-wise stationary time series

• URL: http://arxiv.org/abs/2112.10565v1
• Date: Mon, 20 Dec 2021 14:39:39 GMT
• Title: PyChEst: a Python package for the consistent retrospective estimation of distributional changes in piece-wise stationary time series
• Authors: Azadeh Khaleghi and Lukas Zierahn
• Abstract summary: We introduce PyChEst, a Python package which provides tools for the simultaneous estimation of multiple changepoints in the distribution of piece-wise stationary time series. Nonparametric algorithms implemented are provably consistent in a general framework. We illustrate this distinguishing feature by comparing the performance of the package against state-of-the-art models designed for a setting where the samples are independently and identically distributed.
• Score: 2.398608007786179
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We introduce PyChEst, a Python package which provides tools for the simultaneous estimation of multiple changepoints in the distribution of piece-wise stationary time series. The nonparametric algorithms implemented are provably consistent in a general framework: when the samples are generated by unknown piece-wise stationary processes. In this setting, samples may have long-range dependencies of arbitrary form and the finite-dimensional marginals of any (unknown) fixed size before and after the changepoints may be the same. The strength of the algorithms included in the package is in their ability to consistently detect the changes without imposing any assumptions beyond stationarity on the underlying process distributions. We illustrate this distinguishing feature by comparing the performance of the package against state-of-the-art models designed for a setting where the samples are independently and identically distributed.

Related papers

• SoftCVI: Contrastive variational inference with self-generated soft labels [2.5398014196797614]
  Variational inference and Markov chain Monte Carlo methods are the predominant tools for this task. We introduce Soft Contrastive Variational Inference (SoftCVI), which allows a family of variational objectives to be derived through a contrastive estimation framework. We find that SoftCVI can be used to form objectives which are stable to train and mass-covering, frequently outperforming inference with other variational approaches.
  arXiv  Detail & Related papers  (2024-07-22T14:54:12Z)
• Invariant Subspace Decomposition [10.655331762491613]
  We propose a novel framework for linear conditionals that splits the conditional distribution into a time-invariant and a residual time-dependent component. We show that this decomposition can be utilized both for zero-shot and time-adaptation prediction tasks. We propose a practical estimation procedure, which automatically infers the decomposition using tools from approximate joint matrix diagonalization.
  arXiv  Detail & Related papers  (2024-04-15T17:39:44Z)
• Compatible Transformer for Irregularly Sampled Multivariate Time Series [75.79309862085303]
  We propose a transformer-based encoder to achieve comprehensive temporal-interaction feature learning for each individual sample. We conduct extensive experiments on 3 real-world datasets and validate that the proposed CoFormer significantly and consistently outperforms existing methods.
  arXiv  Detail & Related papers  (2023-10-17T06:29:09Z)
• Deep learning model solves change point detection for multiple change types [69.77452691994712]
  A change points detection aims to catch an abrupt disorder in data distribution. We propose an approach that works in the multiple-distributions scenario.
  arXiv  Detail & Related papers  (2022-04-15T09:44:21Z)
• Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees [7.090165638014331]
  Building on the recently introduced Bayesian Context Trees (BCT) framework, the distributions of different segments in a discrete time series are described as variable-memory Markov chains. Inference for the presence and location of change-points is then performed via Markov chain Monte Carlo sampling. Results on both simulated and real-world data indicate that the proposed methodology performs better than or as well as state-of-the-art techniques.
  arXiv  Detail & Related papers  (2022-03-08T19:03:21Z)
• 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)
• Mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in R [63.26453219947887]
  This package integrates the proglangR package directly into the mlr3 machine-learning framework. One advantage is the use of a consistent recommendations in an overarching machine-learning toolkit.
  arXiv  Detail & Related papers  (2021-10-25T06:48:29Z)
• Multinomial Sampling for Hierarchical Change-Point Detection [0.0]
  We propose a multinomial sampling methodology that improves the detection rate and reduces the delay. Our experiments show results that outperform the baseline method and we also provide an example oriented to a human behavior study.
  arXiv  Detail & Related papers  (2020-07-24T09:18:17Z)
• Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation [72.40827239394565]
  We propose to compute features only at sparsely sampled locations. We then densely reconstruct the feature map with an efficient procedure. The presented network is experimentally shown to save substantial computation while maintaining accuracy over a variety of computer vision tasks.
  arXiv  Detail & Related papers  (2020-03-19T15:36:31Z)
• Batch Stationary Distribution Estimation [98.18201132095066]
  We consider the problem of approximating the stationary distribution of an ergodic Markov chain given a set of sampled transitions. We propose a consistent estimator that is based on recovering a correction ratio function over the given data.
  arXiv  Detail & Related papers  (2020-03-02T09:10:01Z)

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.