SOTAVerified

Time Series Classification

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

Papers

Showing 110 of 697 papers

TitleStatusHype
STACT-Time: Spatio-Temporal Cross Attention for Cine Thyroid Ultrasound Time Series Classification0
TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement LearningCode2
MORIC: CSI Delay-Doppler Decomposition for Robust Wi-Fi-based Human Activity Recognition0
Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers0
Channel-Imposed Fusion: A Simple yet Effective Method for Medical Time Series Classification0
From Images to Signals: Are Large Vision Models Useful for Time Series Analysis?0
FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series ClassificationCode1
DeepConvContext: A Multi-Scale Approach to Timeseries Classification in Human Activity RecognitionCode0
Structured Linear CDEs: Maximally Expressive and Parallel-in-Time Sequence ModelsCode1
QSVM-QNN: Quantum Support Vector Machine Based Quantum Neural Network Learning Algorithm for Brain-Computer Interfacing Systems0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MALSTM-FCNAccuracy0.86Unverified
2FCN-SNLSTAccuracy0.86Unverified
3GP-SigAccuracy0.85Unverified
4SNLSTAccuracy0.84Unverified
5GP-Sig-GRUAccuracy0.83Unverified
6GP-Sig-LSTMAccuracy0.82Unverified
7GP-LSTMAccuracy0.78Unverified
8GP-KConv1DAccuracy0.76Unverified
9GP-GRUAccuracy0.73Unverified