MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification [PDF]
Knowledge Discovery and Data Mining, 2020 Rocket achieves state-of-the-art accuracy for time series classification with a fraction of the computational expense of most existing methods by transforming input time series using random convolutional kernels, and using the transformed features to ...
Angus Dempster +2 more
semanticscholar +1 more source
InceptionTime: Finding AlexNet for time series classification [PDF]
Data mining and knowledge discovery, 2019 This paper brings deep learning at the forefront of research into time series classification (TSC). TSC is the area of machine learning tasked with the categorization (or labelling) of time series.
Hassan Ismail Fawaz +9 more
semanticscholar +1 more source
Time series classification with random temporal features
Journal of King Saud University: Computer and Information Sciences, 2023 Time series classification exists in widespread domains such as EEG/ECG classification, device anomaly detection, and speaker authentication. Although many methods have been proposed, efficient selection of intuitive temporal features to accurately ...
Cun Ji +6 more
doaj +1 more source
MultiRocket: multiple pooling operators and transformations for fast and effective time series classification [PDF]
Data mining and knowledge discovery, 2021 We propose MultiRocket, a fast time series classification (TSC) algorithm that achieves state-of-the-art accuracy with a tiny fraction of the time and without the complex ensembling structure of many state-of-the-art methods.
Chang Wei Tan +3 more
semanticscholar +1 more source
Evaluation of post-hoc interpretability methods in time-series classification [PDF]
Nature Machine Intelligence, 2022 Post-hoc interpretability methods are critical tools to explain neural-network results. Several post-hoc methods have emerged in recent years but they produce different results when applied to a given task, raising the question of which method is the ...
Hugues Turbé +3 more
semanticscholar +1 more source
A Systematic Review of Time Series Classification Techniques Used in Biomedical Applications. [PDF]
Sensors (Basel), 2022 Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, and ingestible and implantable sensors are increasingly used by individuals and clinicians to capture the health ...
Wang WK +12 more
europepmc +2 more sources
FormerTime: Hierarchical Multi-Scale Representations for Multivariate Time Series Classification [PDF]
The Web Conference, 2023 Deep learning-based algorithms, e.g., convolutional networks, have significantly facilitated multivariate time series classification (MTSC) task. Nevertheless, they suffer from the limitation in modeling long-range dependence due to the nature of ...
Mingyue Cheng +5 more
semanticscholar +1 more source
The great multivariate time series classification bake off: a review and experimental evaluation of recent algorithmic advances. [PDF]
Data Min Knowl Discov, 2021 Time Series Classification (TSC) involves building predictive models for a discrete target variable from ordered, real valued, attributes. Over recent years, a new set of TSC algorithms have been developed which have made significant improvement over the
Ruiz AP +4 more
europepmc +3 more sources
Self-Supervised Contrastive Representation Learning for Semi-Supervised Time-Series Classification [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Learning time-series representations when only unlabeled data or few labeled samples are available can be a challenging task. Recently, contrastive self-supervised learning has shown great improvement in extracting useful representations from unlabeled ...
Emadeldeen Eldele +6 more
semanticscholar +1 more source
Graph-Aware Contrasting for Multivariate Time-Series Classification [PDF]
AAAI Conference on Artificial Intelligence, 2023 Contrastive learning, as a self-supervised learning paradigm, becomes popular for Multivariate Time-Series (MTS) classification. It ensures the consistency across different views of unlabeled samples and then learns effective representations for these ...
Yucheng Wang +6 more
semanticscholar +1 more source