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 526–550 of 6748 papers

TitleStatusHype
Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task LearningCode1
Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports DatasetCode1
Conformal Time-series ForecastingCode1
Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image RepresentationCode1
ClaSP - Time Series SegmentationCode1
Classification of Long Sequential Data using Circular Dilated Convolutional Neural NetworksCode1
CKConv: Continuous Kernel Convolution For Sequential DataCode1
Chickenpox Cases in Hungary: a Benchmark Dataset for Spatiotemporal Signal Processing with Graph Neural NetworksCode1
ClaSP -- Parameter-free Time Series SegmentationCode1
Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural NetworksCode1
Data-driven discovery of intrinsic dynamicsCode1
Change Point Detection in Time Series Data using Autoencoders with a Time-Invariant RepresentationCode1
Chaos as an interpretable benchmark for forecasting and data-driven modellingCode1
Causal Forecasting:Generalization Bounds for Autoregressive ModelsCode1
catch22: CAnonical Time-series CHaracteristicsCode1
Causal Recurrent Variational Autoencoder for Medical Time Series GenerationCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
Can LLMs Understand Time Series Anomalies?Code1
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-valuesCode1
Classifying Sequences of Extreme Length with Constant Memory Applied to Malware DetectionCode1
Connecting the Dots: Multivariate Time Series Forecasting with Graph Neural NetworksCode1
Changing Fashion CulturesCode1
A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty QuantificationCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
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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