SOTAVerified

Time Series Analysis

Time Series Analysis is a statistical technique used to analyze and model time-based data. It is used in various fields such as finance, economics, and engineering to analyze patterns and trends in data over time. The goal of time series analysis is to identify the underlying patterns, trends, and seasonality in the data, and to use this information to make informed predictions about future values.

( Image credit: Autoregressive CNNs for Asynchronous Time Series )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 501–525 of 6748 papers

TitleStatusHype
CKConv: Continuous Kernel Convolution For Sequential DataCode1
Motiflets -- Simple and Accurate Detection of Motifs in Time SeriesCode1
Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble: An Improved ROCKET Algorithm for Multivariate Time Series AnalysisCode1
MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed LaplacianCode1
MTSA-SNN: A Multi-modal Time Series Analysis Model Based on Spiking Neural NetworkCode1
ClaSP - Time Series SegmentationCode1
An Open Source and Reproducible Implementation of LSTM and GRU Networks for Time Series ForecastingCode1
Multimodality Multi-Lead ECG Arrhythmia Classification using Self-Supervised LearningCode1
Multi-Modal Temporal Attention Models for Crop Mapping from Satellite Time SeriesCode1
Anomaly Detection of Wind Turbine Time Series using Variational Recurrent AutoencodersCode1
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural NetworksCode1
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image RepresentationCode1
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
Continuous Latent Process FlowsCode1
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant RepresentationCode1
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-valuesCode1
Changing Fashion CulturesCode1
Causal Forecasting:Generalization Bounds for Autoregressive ModelsCode1
catch22: CAnonical Time-series CHaracteristicsCode1
Causal Recurrent Variational Autoencoder for Medical Time Series GenerationCode1
Chaos as an interpretable benchmark for forecasting and data-driven modellingCode1
Can LLMs Understand Time Series Anomalies?Code1
AGNet: Weighing Black Holes with Machine LearningCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
AGNet: Weighing Black Holes with Deep LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47—Unverified
2GRU-D - APC (n = 1)F127.3—Unverified
3GRU-APC (n = 1)F125.7—Unverified
4GRU-DF122.5—Unverified
5GRUF122.3—Unverified
6GRU-SimpleF122.2—Unverified
7GRU-MeanF122.1—Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51—Unverified
2ViT% Test Accuracy98.11—Unverified
3FlexTCN-4% Test Accuracy97.73—Unverified
4MatchboxNet% Test Accuracy97.4—Unverified
5CKCNN (100k)% Test Accuracy95.27—Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73—Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13—Unverified