Early Time-Series Classification Algorithms: An Empirical Comparison
- URL: http://arxiv.org/abs/2203.01628v1
- Date: Thu, 3 Mar 2022 10:43:56 GMT
- Title: Early Time-Series Classification Algorithms: An Empirical Comparison
- Authors: Charilaos Akasiadis and Evgenios Kladis and Evangelos Michelioudakis
and Elias Alevizos and Alexander Artikis
- Abstract summary: 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.
- Score: 59.82930053437851
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Early Time-Series Classification (ETSC) is the task of predicting the class
of incoming time-series by observing as few measurements as possible. Such
methods can be employed to obtain classification forecasts in many
time-critical applications. However, available techniques are not equally
suitable for every problem, since differentiations in the data characteristics
can impact algorithm performance in terms of earliness, accuracy, F1-score, and
training time. We evaluate six existing ETSC algorithms on publicly available
data, as well as on two newly introduced datasets originating from the life
sciences and maritime domains. Our goal is to provide a framework for the
evaluation and comparison of ETSC algorithms and to obtain intuition on how
such approaches perform on real-life applications. The presented framework may
also serve as a benchmark for new related techniques.
Related papers
- RHiOTS: A Framework for Evaluating Hierarchical Time Series Forecasting Algorithms [0.393259574660092]
RHiOTS is designed to assess the robustness of hierarchical time series forecasting models and algorithms on real-world datasets.
RHiOTS incorporates an innovative visualization component, turning complex, multidimensional robustness evaluation results into intuitive, easily interpretable visuals.
Our findings show that traditional statistical methods are more robust than state-of-the-art deep learning algorithms, except when the transformation effect is highly disruptive.
arXiv Detail & Related papers (2024-08-06T18:52:15Z) - 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) - Improving the Evaluation and Actionability of Explanation Methods for Multivariate Time Series Classification [4.588028371034407]
We focus on analyzing InterpretTime, a recent evaluation methodology for attribution methods applied to MTSC.
We showcase some significant weaknesses of the original methodology and propose ideas to improve its accuracy and efficiency.
We find that perturbation-based methods such as SHAP and Feature Ablation work well across a set of datasets.
arXiv Detail & Related papers (2024-06-18T11:18:46Z) - 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) - Multivariate Time Series Early Classification Across Channel and Time
Dimensions [3.5786621294068373]
We propose a more flexible early classification pipeline that offers a more granular consideration of input channels.
Our method can enhance the early classification paradigm by achieving improved accuracy for equal input utilization.
arXiv Detail & Related papers (2023-06-26T11:30:33Z) - Exogenous Data in Forecasting: FARM -- A New Measure for Relevance
Evaluation [62.997667081978825]
We introduce a new approach named FARM - Forward Relevance Aligned Metric.
Our forward method relies on an angular measure that compares changes in subsequent data points to align time-warped series.
As a first validation step, we present the application of our FARM approach to synthetic but representative signals.
arXiv Detail & Related papers (2023-04-21T15:22:33Z) - The FreshPRINCE: A Simple Transformation Based Pipeline Time Series
Classifier [0.0]
We look at whether the complexity of the algorithms considered state of the art is really necessary.
Many times the first approach suggested is a simple pipeline of summary statistics or other time series feature extraction approaches.
We test these approaches on the UCR time series dataset archive, looking to see if TSC literature has overlooked the effectiveness of these approaches.
arXiv Detail & Related papers (2022-01-28T11:23:58Z) - Cluster-and-Conquer: A Framework For Time-Series Forecasting [94.63501563413725]
We propose a three-stage framework for forecasting high-dimensional time-series data.
Our framework is highly general, allowing for any time-series forecasting and clustering method to be used in each step.
When instantiated with simple linear autoregressive models, we are able to achieve state-of-the-art results on several benchmark datasets.
arXiv Detail & Related papers (2021-10-26T20:41:19Z) - Doing Great at Estimating CATE? On the Neglected Assumptions in
Benchmark Comparisons of Treatment Effect Estimators [91.3755431537592]
We show that even in arguably the simplest setting, estimation under ignorability assumptions can be misleading.
We consider two popular machine learning benchmark datasets for evaluation of heterogeneous treatment effect estimators.
We highlight that the inherent characteristics of the benchmark datasets favor some algorithms over others.
arXiv Detail & Related papers (2021-07-28T13:21:27Z) - 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)
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.