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 1–10 of 697 papers

TitleStatusHype
STACT-Time: Spatio-Temporal Cross Attention for Cine Thyroid Ultrasound Time Series Classification—0
TimeMaster: Training Time-Series Multimodal LLMs to Reason via Reinforcement LearningCode2
MORIC: CSI Delay-Doppler Decomposition for Robust Wi-Fi-based Human Activity Recognition—0
Time Series Representations for Classification Lie Hidden in Pretrained Vision Transformers—0
Channel-Imposed Fusion: A Simple yet Effective Method for Medical Time Series Classification—0
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 Systems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1FCN-SNLSTAccuracy0.99—Unverified
2GP-Sig-LSTMAccuracy0.98—Unverified
3GP-Sig-GRUAccuracy0.98—Unverified
4SNLSTAccuracy0.97—Unverified
5MALSTM-FCNAccuracy0.96—Unverified
6GP-GRUAccuracy0.95—Unverified
7GP-SigAccuracy0.93—Unverified
8GP-LSTMAccuracy0.88—Unverified
9GP-KConv1DAccuracy0.78—Unverified