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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/rameshdoddaiah/demux

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Class-Specific Explainability for Deep Time Series Classifiers

https://github.com/rbordoloi/mudra

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Multivariate Functional Linear Discriminant Analysis for the Classification of Short Time Series with Missing Data

https://github.com/realwangjiahao/tabletime

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TableTime: Reformulating Time Series Classification as Zero-Shot Table Understanding via Large Language Models

https://github.com/reml-lab/mTAN

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Multi-Time Attention Networks for Irregularly Sampled Time Series

https://github.com/rmchurch/disruptCNN

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Deep convolutional neural networks for multi-scale time-series classification and application to disruption prediction in fusion devices

https://github.com/salehinejad/srocket

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S-Rocket: Selective Random Convolution Kernels for Time Series Classification

https://github.com/sathvikb007/ECG-TSC-FSL

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Similarity Learning based Few Shot Learning for ECG Time Series Classification

https://github.com/senyuanya/mmlc

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Multi-task Meta Label Correction for Time Series Prediction

https://github.com/shaowuchen/p-rocket

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POCKET: Pruning Random Convolution Kernels for Time Series Classification from a Feature Selection Perspective

https://github.com/shaowuchen/pocket

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POCKET: Pruning Random Convolution Kernels for Time Series Classification from a Feature Selection Perspective

https://github.com/sheoyon-jhin/ancde

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Attentive Neural Controlled Differential Equations for Time-series Classification and Forecasting

https://github.com/shivam-grover/s3-timeseries

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Segment, Shuffle, and Stitch: A Simple Layer for Improving Time-Series Representations

https://github.com/sindhura97/STraTS

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Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series

https://github.com/sktime/sktime

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Fast and Accurate Time Series Classification with WEASEL

https://github.com/spencerlabaq/aipt

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AI-driven Java Performance Testing: Balancing Result Quality with Testing Time

https://github.com/stevcabello/r-STSF

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Fast, Accurate and Interpretable Time Series Classification Through Randomization

https://github.com/suehiro93/MILIMS_NECO

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Theory and Algorithms for Shapelet-based Multiple-Instance Learning

https://github.com/sylvaincom/astride

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ASTRIDE: Adaptive Symbolization for Time Series Databases

https://github.com/tahabelkhouja/robust-training-for-time-series

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Training Robust Deep Models for Time-Series Domain: Novel Algorithms and Theoretical Analysis

https://github.com/tallamjr/astronet

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Paying Attention to Astronomical Transients: Introducing the Time-series Transformer for Photometric Classification

https://github.com/teptwomey/deep_learning_architectures_for_fscv

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Deep Learning Architectures for FSCV, a Comparison

https://github.com/tgcsaba/GPSig

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Bayesian Learning from Sequential Data using Gaussian Processes with Signature Covariances

https://github.com/tgcsaba/seq2tens

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Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor Projections

https://github.com/tian0426/frera

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FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification

https://github.com/titu1994/LSTM-FCN

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LSTM Fully Convolutional Networks for Time Series Classification

https://github.com/tk-rusch/lem

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Long Expressive Memory for Sequence Modeling

https://github.com/tk-rusch/unicornn

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UnICORNN: A recurrent model for learning very long time dependencies

https://github.com/twkillian/edict

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Continuous Time Evidential Distributions for Irregular Time Series

https://github.com/uchidalab/time-series-transferability

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Model Selection with a Shapelet-based Distance Measure for Multi-source Transfer Learning in Time Series Classification

https://github.com/uchidalab/time_series_augmentation

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Time Series Data Augmentation for Neural Networks by Time Warping with a Discriminative Teacher

https://github.com/uiucsn/elasticc-classification

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ORACLE: A Real-Time, Hierarchical, Deep-Learning Photometric Classifier for the LSST

https://github.com/vanyalzr/pyspikelib

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Spikebench: An open benchmark for spike train time-series classification

https://github.com/vfeofanov/mantis

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Mantis: Lightweight Calibrated Foundation Model for User-Friendly Time Series Classification

https://github.com/victormvy/sigma-convkernels

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A hybrid feature learning approach based on convolutional kernels for ATM fault prediction using event-log data

https://github.com/vinayp17/vlm_tsc

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On the Feasibility of Vision-Language Models for Time-Series Classification

https://github.com/visual-xai-for-time-series/attribution-stability-indicator

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Introducing the Attribution Stability Indicator: a Measure for Time Series XAI Attributions

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