The Semantic Adjacency Criterion in Time Intervals Mining
- URL: http://arxiv.org/abs/2101.03842v1
- Date: Mon, 11 Jan 2021 12:23:49 GMT
- Title: The Semantic Adjacency Criterion in Time Intervals Mining
- Authors: Alexander Shknevsky, Yuval Shahar, Robert Moskovitch
- Abstract summary: We propose a new pruning constraint during a frequent temporal-pattern discovery process, the Semantic Adjacency Criterion [SAC]
We have defined three SAC versions, and tested their effect in three medical domains.
- Score: 70.13948372218849
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Frequent temporal patterns discovered in time-interval-based multivariate
data, although syntactically correct, might be non-transparent: For some
pattern instances, there might exist intervals for the same entity that
contradict the pattern's usual meaning. We conjecture that non-transparent
patterns are also less useful as classification or prediction features. We
propose a new pruning constraint during a frequent temporal-pattern discovery
process, the Semantic Adjacency Criterion [SAC], which exploits domain
knowledge to filter out patterns that contain potentially semantically
contradictory components. We have defined three SAC versions, and tested their
effect in three medical domains. We embedded these criteria in a
frequent-temporal-pattern discovery framework. Previously, we had informally
presented the SAC principle and showed that using it to prune patterns enhances
the repeatability of their discovery in the same clinical domain. Here, we
define formally the semantics of three SAC variations, and compare the use of
the set of pruned patterns to the use of the complete set of discovered
patterns, as features for classification and prediction tasks in three
different medical domains. We induced four classifiers for each task, using
four machine-learning methods: Random Forests, Naive Bayes, SVM, and Logistic
Regression. The features were frequent temporal patterns discovered in each
data set. SAC-based temporal pattern-discovery reduced by up to 97% the number
of discovered patterns and by up to 98% the discovery runtime. But the
classification and prediction performance of the reduced SAC-based
pattern-based features set, was as good as when using the complete set. Using
SAC can significantly reduce the number of discovered frequent interval-based
temporal patterns, and the corresponding computational effort, without losing
classification or prediction performance.
Related papers
- Causal Discovery-Driven Change Point Detection in Time Series [32.424281626708336]
Change point detection in time series seeks to identify times when the probability distribution of time series changes.
In practical applications, we may be interested only in certain components of the time series, exploring abrupt changes in their distributions.
arXiv Detail & Related papers (2024-07-10T00:54:42Z) - TimeSiam: A Pre-Training Framework for Siamese Time-Series Modeling [67.02157180089573]
Time series pre-training has recently garnered wide attention for its potential to reduce labeling expenses and benefit various downstream tasks.
This paper proposes TimeSiam as a simple but effective self-supervised pre-training framework for Time series based on Siamese networks.
arXiv Detail & Related papers (2024-02-04T13:10:51Z) - GaitASMS: Gait Recognition by Adaptive Structured Spatial Representation
and Multi-Scale Temporal Aggregation [2.0444600042188448]
Gait recognition is one of the most promising video-based biometric technologies.
We propose a novel gait recognition framework, denoted as GaitASMS.
It can effectively extract the adaptive structured spatial representations and naturally aggregate the multi-scale temporal information.
arXiv Detail & Related papers (2023-07-29T13:03:17Z) - Ensembled Prediction Intervals for Causal Outcomes Under Hidden
Confounding [49.1865229301561]
We present a simple approach to partial identification using existing causal sensitivity models and show empirically that Caus-Modens gives tighter outcome intervals.
The last of our three diverse benchmarks is a novel usage of GPT-4 for observational experiments with unknown but probeable ground truth.
arXiv Detail & Related papers (2023-06-15T21:42:40Z) - T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in
Disease Progression [82.85825388788567]
We develop a novel temporal clustering method, T-Phenotype, to discover phenotypes of predictive temporal patterns from labeled time-series data.
We show that T-Phenotype achieves the best phenotype discovery performance over all the evaluated baselines.
arXiv Detail & Related papers (2023-02-24T13:30:35Z) - Finding Short Signals in Long Irregular Time Series with Continuous-Time
Attention Policy Networks [18.401817124823832]
Irregularly-sampled time series (ITS) are native to high-impact domains like healthcare, where measurements are collected over time at uneven intervals.
We propose CAT, a model that classifies multivariate ITS by explicitly seeking highly-relevant portions of an input series' timeline.
Using synthetic and real data, we find that CAT outperforms ten state-of-the-art methods by finding short signals in long irregular time series.
arXiv Detail & Related papers (2023-02-08T13:44:36Z) - A Pattern Discovery Approach to Multivariate Time Series Forecasting [27.130141538089152]
State-of-the-art deep learning methods fail to construct models for full time series because model complexity grows exponentially with time series length.
We propose a novel pattern discovery method that can automatically capture diverse and complex time series patterns.
We also propose a learnable correlation matrix, that enables the model to capture distinct correlations among multiple time series.
arXiv Detail & Related papers (2022-12-20T14:54:04Z) - Novel Features for Time Series Analysis: A Complex Networks Approach [62.997667081978825]
Time series data are ubiquitous in several domains as climate, economics and health care.
Recent conceptual approach relies on time series mapping to complex networks.
Network analysis can be used to characterize different types of time series.
arXiv Detail & Related papers (2021-10-11T13:46:28Z) - Anomaly Transformer: Time Series Anomaly Detection with Association
Discrepancy [68.86835407617778]
Anomaly Transformer achieves state-of-the-art performance on six unsupervised time series anomaly detection benchmarks.
Anomaly Transformer achieves state-of-the-art performance on six unsupervised time series anomaly detection benchmarks.
arXiv Detail & Related papers (2021-10-06T10:33:55Z) - 3D Iterative Spatiotemporal Filtering for Classification of
Multitemporal Satellite Data Sets [4.6998356311022285]
3D geometric features have been shown to be stable for assessing differences across the temporal data set.
In this article we investigate he use of a multitemporal orthophoto and digital surface model derived from satellite data fortemporal classification.
arXiv Detail & Related papers (2021-07-01T16:26:52Z)
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.