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Time Series Classification

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하위 태스크: 2
평가표: 52
보존된 평가 기록: 231

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

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/WenjieDu/TSDB

공식 코드

PyPOTS: A Python Toolbox for Data Mining on Partially-Observed Time Series

https://github.com/Wensi-Tang/DPSN

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Interpretable Time-series Classification on Few-shot Samples

https://github.com/Wensi-Tang/OS-CNN

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Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification

https://github.com/Yiru-Jiao/spclt

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Structure-preserving contrastive learning for spatial time series

https://github.com/YuliaRubanova/latent_ode

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Latent ODEs for Irregularly-Sampled Time Series

https://github.com/ZLiu21/SoftShape

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Learning Soft Sparse Shapes for Efficient Time-Series Classification

https://github.com/ZZKnight/Project-3_IPC-SHM2020

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Condition Assessment of Stay Cables through Enhanced Time Series Classification Using a Deep Learning Approach

https://github.com/ZZUFaceBookDL/GTN

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Gated Transformer Networks for Multivariate Time Series Classification

https://github.com/a11to1n3/tsem

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TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time Series

https://github.com/adityalab/samay

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Large Pre-trained time series models for cross-domain Time series analysis tasks

https://github.com/adityamakkar000/ASL-Sign-Research

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SignSpeak: Open-Source Time Series Classification for ASL Translation

https://github.com/adrienpetralia/ApplianceDetectionBenchmark

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Appliance Detection Using Very Low-Frequency Smart Meter Time Series

https://github.com/adrienpetralia/transapp

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ADF & TransApp: A Transformer-Based Framework for Appliance Detection Using Smart Meter Consumption Series

https://github.com/aeon-toolkit/aeon

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Bake off redux: a review and experimental evaluation of recent time series classification algorithms

https://github.com/agustdd/floss

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Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach

https://github.com/ai4healthuol/causalconceptts

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CausalConceptTS: Causal Attributions for Time Series Classification using High Fidelity Diffusion Models

https://github.com/aikunyi/MedGNN

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MedGNN: Towards Multi-resolution Spatiotemporal Graph Learning for Medical Time Series Classification

https://github.com/alflsowl12/LEAP

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Learnable Path in Neural Controlled Differential Equations

https://github.com/angus924/hydra

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HYDRA: Competing convolutional kernels for fast and accurate time series classification

https://github.com/angus924/minirocket

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MINIROCKET: A Very Fast (Almost) Deterministic Transform for Time Series Classification

https://github.com/angus924/quant

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QUANT: A Minimalist Interval Method for Time Series Classification

https://github.com/angus924/rocket

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ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels

https://github.com/arvintashakori/SemiPFL

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SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence

https://github.com/astrolabsoftware/fink-science

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Finding active galactic nuclei through Fink

https://github.com/baraline/convst

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Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets

https://github.com/bbj-lab/protoecgnet

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ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning

https://github.com/benjamin-walker/log-neural-cdes

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Log Neural Controlled Differential Equations: The Lie Brackets Make a Difference

https://github.com/benjamin-walker/structured-linear-cdes

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Structured Linear CDEs: Maximally Expressive and Parallel-in-Time Sequence Models

https://github.com/bidyutsaha/tinytnas

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TinyTNAS: GPU-Free, Time-Bound, Hardware-Aware Neural Architecture Search for TinyML Time Series Classification

https://github.com/blacksnail789521/timedrl

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TimeDRL: Disentangled Representation Learning for Multivariate Time-Series

https://github.com/blue-yonder/tsfresh

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Distributed and parallel time series feature extraction for industrial big data applications

https://github.com/cauchyturing/Deconv_SAX

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Representation Learning with Deconvolution for Multivariate Time Series Classification and Visualization

https://github.com/cauchyturing/UCR_Time_Series_Classification_Deep_Learning_Baseline

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Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline

https://github.com/cedricdonie/tsc-for-wrist-motion-pd-detection

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Time Series Classification for Detecting Parkinson's Disease from Wrist Motions

https://github.com/chang-george-dong/kan-for-tsc

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Kolmogorov-Arnold Networks (KAN) for Time Series Classification and Robust Analysis

https://github.com/chlubba/catch22

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catch22: CAnonical Time-series CHaracteristics

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