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

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Showing 601–625 of 6748 papers

TitleStatusHype
Development of Interpretable Machine Learning Models to Detect Arrhythmia based on ECG DataCode1
Differentiable Compositional Kernel Learning for Gaussian ProcessesCode1
A Generalised Signature Method for Multivariate Time Series Feature ExtractionCode1
Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic ForecastingCode1
Diffusion models for missing value imputation in tabular dataCode1
An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly DetectionCode1
Anytime-valid off-policy inference for contextual banditsCode1
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICACode1
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence ModelsCode1
DL-Traff: Survey and Benchmark of Deep Learning Models for Urban Traffic PredictionCode1
BolT: Fused Window Transformers for fMRI Time Series AnalysisCode1
Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series TransformerCode1
InceptionTime: Finding AlexNet for Time Series ClassificationCode1
DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series DataCode1
A Physiology-Driven Computational Model for Post-Cardiac Arrest Outcome PredictionCode1
Dynamic Data Augmentation with Gating Networks for Time Series RecognitionCode1
Dynamic Graph Learning-Neural Network for Multivariate Time Series ModelingCode1
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson SamplingCode1
Early Abandoning and Pruning for Elastic Distances including Dynamic Time WarpingCode1
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signalsCode1
General Evaluation for Instruction Conditioned Navigation using Dynamic Time WarpingCode1
Benchmarking Deep Learning Interpretability in Time Series PredictionsCode1
Adaptive Graph Convolutional Recurrent Network for Traffic ForecastingCode1
Efficient Optimization of Echo State Networks for Time Series DatasetsCode1
A general framework for multi-step ahead adaptive conformal heteroscedastic time series forecastingCode1
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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