Pattern Sampling for Shapelet-based Time Series Classification

• URL: http://arxiv.org/abs/2102.08498v1
• Date: Tue, 16 Feb 2021 23:35:10 GMT
• Title: Pattern Sampling for Shapelet-based Time Series Classification
• Authors: Atif Raza, Stefan Kramer
• Abstract summary: Subsequence-based time series classification algorithms provide accurate and interpretable models. These algorithms are based on exhaustive search for highly discriminative subsequences. Pattern sampling has been proposed as an effective alternative to mitigate the pattern explosion phenomenon.
• Score: 4.94950858749529
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Subsequence-based time series classification algorithms provide accurate and interpretable models, but training these models is extremely computation intensive. The asymptotic time complexity of subsequence-based algorithms remains a higher-order polynomial, because these algorithms are based on exhaustive search for highly discriminative subsequences. Pattern sampling has been proposed as an effective alternative to mitigate the pattern explosion phenomenon. Therefore, we employ pattern sampling to extract discriminative features from discretized time series data. A weighted trie is created based on the discretized time series data to sample highly discriminative patterns. These sampled patterns are used to identify the shapelets which are used to transform the time series classification problem into a feature-based classification problem. Finally, a classification model can be trained using any off-the-shelf algorithm. Creating a pattern sampler requires a small number of patterns to be evaluated compared to an exhaustive search as employed by previous approaches. Compared to previously proposed algorithms, our approach requires considerably less computational and memory resources. Experiments demonstrate how the proposed approach fares in terms of classification accuracy and runtime performance.

Related papers

• An Efficient Algorithm for Clustered Multi-Task Compressive Sensing [60.70532293880842]
  Clustered multi-task compressive sensing is a hierarchical model that solves multiple compressive sensing tasks. The existing inference algorithm for this model is computationally expensive and does not scale well in high dimensions. We propose a new algorithm that substantially accelerates model inference by avoiding the need to explicitly compute these covariance matrices.
  arXiv  Detail & Related papers  (2023-09-30T15:57:14Z)
• Best-Subset Selection in Generalized Linear Models: A Fast and Consistent Algorithm via Splicing Technique [0.6338047104436422]
  Best subset section has been widely regarded as the Holy Grail of problems of this type. We proposed and illustrated an algorithm for best subset recovery in mild conditions. Our implementation achieves approximately a fourfold speedup compared to popular variable selection toolkits.
  arXiv  Detail & Related papers  (2023-08-01T03:11:31Z)
• Efficient Failure Pattern Identification of Predictive Algorithms [15.02620042972929]
  We propose a human-machine collaborative framework that consists of a team of human annotators and a sequential recommendation algorithm. The results empirically demonstrate the competitive performance of our framework on multiple datasets at various signal-to-noise ratios.
  arXiv  Detail & Related papers  (2023-06-01T14:54:42Z)
• Quickest Change Detection for Unnormalized Statistical Models [36.6516991850508]
  This paper develops a new variant of the classical Cumulative Sum (CUSUM) algorithm for the quickest change detection. The SCUSUM algorithm allows the applications of change detection for unnormalized statistical models.
  arXiv  Detail & Related papers  (2023-02-01T05:27:34Z)
• Time Series Clustering with an EM algorithm for Mixtures of Linear Gaussian State Space Models [0.0]
  We propose a novel model-based time series clustering method with mixtures of linear Gaussian state space models. The proposed method uses a new expectation-maximization algorithm for the mixture model to estimate the model parameters. Experiments on a simulated dataset demonstrate the effectiveness of the method in clustering, parameter estimation, and model selection.
  arXiv  Detail & Related papers  (2022-08-25T07:41:23Z)
• Machine Learning for Online Algorithm Selection under Censored Feedback [71.6879432974126]
  In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. We adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon.
  arXiv  Detail & Related papers  (2021-09-13T18:10:52Z)
• Local policy search with Bayesian optimization [73.0364959221845]
  Reinforcement learning aims to find an optimal policy by interaction with an environment. Policy gradients for local search are often obtained from random perturbations. We develop an algorithm utilizing a probabilistic model of the objective function and its gradient.
  arXiv  Detail & Related papers  (2021-06-22T16:07:02Z)
• Goal-directed Generation of Discrete Structures with Conditional Generative Models [85.51463588099556]
  We introduce a novel approach to directly optimize a reinforcement learning objective, maximizing an expected reward. We test our methodology on two tasks: generating molecules with user-defined properties and identifying short python expressions which evaluate to a given target value.
  arXiv  Detail & Related papers  (2020-10-05T20:03:13Z)
• Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis [75.64261155172856]
  survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.
  arXiv  Detail & Related papers  (2020-07-06T15:20:17Z)
• Interpretable Time Series Classification using Linear Models and Multi-resolution Multi-domain Symbolic Representations [6.6147550436077776]
  We propose new time series classification algorithms to address gaps in current approaches. Our approach is based on symbolic representations of time series, efficient sequence mining algorithms and linear classification models. Our models are as accurate as deep learning models but are more efficient regarding running time and memory, can work with variable-length time series and can be interpreted by highlighting the discriminative symbolic features on the original time series.
  arXiv  Detail & Related papers  (2020-05-31T15:32:08Z)
• Learning Gaussian Graphical Models via Multiplicative Weights [54.252053139374205]
  We adapt an algorithm of Klivans and Meka based on the method of multiplicative weight updates. The algorithm enjoys a sample complexity bound that is qualitatively similar to others in the literature. It has a low runtime $O(mp2)$ in the case of $m$ samples and $p$ nodes, and can trivially be implemented in an online manner.
  arXiv  Detail & Related papers  (2020-02-20T10:50:58Z)

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.