Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion

• URL: http://arxiv.org/abs/2308.01071v1
• Date: Wed, 2 Aug 2023 10:46:42 GMT
• Title: Automatic Feature Engineering for Time Series Classification: Evaluation and Discussion
• Authors: Aur\'elien Renault and Alexis Bondu and Vincent Lemaire and Dominique Gay
• Abstract summary: Time Series Classification (TSC) is a crucial and challenging problem in data science and knowledge engineering. Several tools for extracting unsupervised informative summary statistics, aka features, from time series have been designed in the recent years. In this article, we propose a simple TSC process to evaluate the potential predictive performance of the feature sets obtained with existing feature engineering tools.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Time Series Classification (TSC) has received much attention in the past two decades and is still a crucial and challenging problem in data science and knowledge engineering. Indeed, along with the increasing availability of time series data, many TSC algorithms have been suggested by the research community in the literature. Besides state-of-the-art methods based on similarity measures, intervals, shapelets, dictionaries, deep learning methods or hybrid ensemble methods, several tools for extracting unsupervised informative summary statistics, aka features, from time series have been designed in the recent years. Originally designed for descriptive analysis and visualization of time series with informative and interpretable features, very few of these feature engineering tools have been benchmarked for TSC problems and compared with state-of-the-art TSC algorithms in terms of predictive performance. In this article, we aim at filling this gap and propose a simple TSC process to evaluate the potential predictive performance of the feature sets obtained with existing feature engineering tools. Thus, we present an empirical study of 11 feature engineering tools branched with 9 supervised classifiers over 112 time series data sets. The analysis of the results of more than 10000 learning experiments indicate that feature-based methods perform as accurately as current state-of-the-art TSC algorithms, and thus should rightfully be considered further in the TSC literature.

Related papers

• TSI-Bench: Benchmarking Time Series Imputation [52.27004336123575]
  TSI-Bench is a comprehensive benchmark suite for time series imputation utilizing deep learning techniques. The TSI-Bench pipeline standardizes experimental settings to enable fair evaluation of imputation algorithms. TSI-Bench innovatively provides a systematic paradigm to tailor time series forecasting algorithms for imputation purposes.
  arXiv  Detail & Related papers  (2024-06-18T16:07:33Z)
• Automated Contrastive Learning Strategy Search for Time Series [48.68664732145665]
  Contrastive Learning (CL) has become a predominant representation learning paradigm for time series. We present an Automated Machine Learning (AutoML) practice at Microsoft, which automatically learns to contrast learn representations for various time series datasets.
  arXiv  Detail & Related papers  (2024-03-19T11:24:14Z)
• Supervised Time Series Classification for Anomaly Detection in Subsea Engineering [0.0]
  We investigate the use of supervised machine learning classification algorithms on simulated data based on a physical system with two states: Intact and Broken. We provide a comprehensive discussion of the preprocessing of temporal data, using measures of statistical dispersion and dimension reduction techniques. We conclude with a comparison of the various methods based on different performance metrics, showing the advantage of using machine learning techniques as a tool in decision making.
  arXiv  Detail & Related papers  (2024-03-12T18:25:10Z)
• Self-Supervised Learning for Time Series Analysis: Taxonomy, Progress, and Prospects [84.6945070729684]
  Self-supervised learning (SSL) has recently achieved impressive performance on various time series tasks. This article reviews current state-of-the-art SSL methods for time series data.
  arXiv  Detail & Related papers  (2023-06-16T18:23:10Z)
• Analyzing categorical time series with the R package ctsfeatures [0.0]
  The R package ctsfeatures offers users a set of useful tools for analyzing categorical time series. The output of some functions can be employed to perform traditional machine learning tasks including clustering, classification and outlier detection.
  arXiv  Detail & Related papers  (2023-04-24T16:16:56Z)
• Enhancing Multivariate Time Series Classifiers through Self-Attention and Relative Positioning Infusion [4.18804572788063]
  Time Series Classification (TSC) is an important and challenging task for many visual computing applications. We propose two novel attention blocks that can enhance deep learning-based TSC approaches. We show that adding the proposed attention blocks improves base models' average accuracy by up to 3.6%.
  arXiv  Detail & Related papers  (2023-02-13T20:50:34Z)
• A Review of Open Source Software Tools for Time Series Analysis [0.0]
  This paper describes a typical Time Series Analysis (TSA) framework with an architecture and lists the main features of TSA framework. Overall, this article considered 60 time series analysis tools, and 32 of which provided forecasting modules, and 21 packages included anomaly detection.
  arXiv  Detail & Related papers  (2022-03-10T07:12:20Z)
• Early Time-Series Classification Algorithms: An Empirical Comparison [59.82930053437851]
  Early Time-Series Classification (ETSC) is the task of predicting the class of incoming time-series by observing as few measurements as possible. We evaluate six existing ETSC algorithms on publicly available data, as well as on two newly introduced datasets.
  arXiv  Detail & Related papers  (2022-03-03T10:43:56Z)
• Towards Similarity-Aware Time-Series Classification [51.2400839966489]
  We study time-series classification (TSC), a fundamental task of time-series data mining. We propose Similarity-Aware Time-Series Classification (SimTSC), a framework that models similarity information with graph neural networks (GNNs)
  arXiv  Detail & Related papers  (2022-01-05T02:14:57Z)
• 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)

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.