Time Series Classification
243 papers with code • 51 benchmarks • 17 datasets
Time Series Classification is a general task that can be useful across many subject-matter domains and applications. The overall goal is to identify a time series as coming from one of possibly many sources or predefined groups, using labeled training data. That is, in this setting we conduct supervised learning, where the different time series sources are considered known.
Source: Nonlinear Time Series Classification Using Bispectrum-based Deep Convolutional Neural Networks
Benchmarks
These leaderboards are used to track progress in Time Series Classification
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Libraries
Use these libraries to find Time Series Classification models and implementationsDatasets
SHAPES
UCI Machine Learning Repository
PhysioNet Challenge 2012
US Economy
BorealTC
Latest papers
Series2Vec: Similarity-based Self-supervised Representation Learning for Time Series Classification
Our evaluation of Series2Vec on nine large real-world datasets, along with the UCR/UEA archive, shows enhanced performance compared to current state-of-the-art self-supervised techniques for time series.
TimeDRL: Disentangled Representation Learning for Multivariate Time-Series
Multivariate time-series data in numerous real-world applications (e. g., healthcare and industry) are informative but challenging due to the lack of labels and high dimensionality.
FRUITS: Feature Extraction Using Iterated Sums for Time Series Classification
We introduce a pipeline for time series classification that extracts features based on the iterated-sums signature (ISS) and then applies a linear classifier.
Finding Foundation Models for Time Series Classification with a PreText Task
msd-irimas/domainfoundationmodelstsc
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Over the past decade, Time Series Classification (TSC) has gained an increasing attention.
Inherently Interpretable Time Series Classification via Multiple Instance Learning
jaearly/miltimeseriesclassification
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Conventional Time Series Classification (TSC) methods are often black boxes that obscure inherent interpretation of their decision-making processes.
XTSC-Bench: Quantitative Benchmarking for Explainers on Time Series Classification
Despite the growing body of work on explainable machine learning in time series classification (TSC), it remains unclear how to evaluate different explainability methods.
Temporal Convolutional Explorer Helps Understand 1D-CNN's Learning Behavior in Time Series Classification from Frequency Domain
jrzhang33/tce
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While one-dimensional convolutional neural networks (1D-CNNs) have been empirically proven effective in time series classification tasks, we find that there remain undesirable outcomes that could arise in their application, motivating us to further investigate and understand their underlying mechanisms.
Introducing the Attribution Stability Indicator: a Measure for Time Series XAI Attributions
visual-xai-for-time-series/attribution-stability-indicator
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Given the increasing amount and general complexity of time series data in domains such as finance, weather forecasting, and healthcare, there is a growing need for state-of-the-art performance models that can provide interpretable insights into underlying patterns and relationships.
Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels
When applied to the largest binary UCR dataset, Detach-ROCKET is able to improve test accuracy by $0. 6\%$ while reducing the number of features by $98. 9\%$.
Parallelizing non-linear sequential models over the sequence length
Sequential models, such as Recurrent Neural Networks and Neural Ordinary Differential Equations, have long suffered from slow training due to their inherent sequential nature.
