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 376–400 of 6748 papers

TitleStatusHype
Extraction of instantaneous frequencies and amplitudes in nonstationary time-series dataCode1
A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking ApplicationsCode1
BolT: Fused Window Transformers for fMRI Time Series AnalysisCode1
A Deep Learning Approach to Analyzing Continuous-Time SystemsCode1
Building Calibrated Deep Models via Uncertainty Matching with Auxiliary Interval PredictorsCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
FC-GAGA: Fully Connected Gated Graph Architecture for Spatio-Temporal Traffic ForecastingCode1
CAMul: Calibrated and Accurate Multi-view Time-Series ForecastingCode1
Calibration of Google Trends Time SeriesCode1
A deep learning architecture for temporal sleep stage classification using multivariate and multimodal time seriesCode1
Can LLMs Understand Time Series Anomalies?Code1
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image RepresentationCode1
A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series DataCode1
catch22: CAnonical Time-series CHaracteristicsCode1
Few-Shot Forecasting of Time-Series with Heterogeneous ChannelsCode1
Causal Forecasting:Generalization Bounds for Autoregressive ModelsCode1
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-valuesCode1
Causal Recurrent Variational Autoencoder for Medical Time Series GenerationCode1
Finding active galactic nuclei through FinkCode1
Adaptive Conformal Predictions for Time SeriesCode1
First De-Trend then Attend: Rethinking Attention for Time-Series ForecastingCode1
FlexConv: Continuous Kernel Convolutions with Differentiable Kernel SizesCode1
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series DataCode1
Deep Latent State Space Models for Time-Series GenerationCode1
A Physiology-Driven Computational Model for Post-Cardiac Arrest Outcome PredictionCode1
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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