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 2226–2250 of 6748 papers

TitleStatusHype
Classifying Frames at the Sentence Level in News Articles—0
Classifying Contaminated Cell Cultures using Time Series Features—0
Application Research On Real-Time Perception Of Device Performance Status—0
Classifiers With a Reject Option for Early Time-Series Classification—0
Classification with the matrix-variate-t distribution—0
Application of Time Series Analysis to Traffic Accidents in Los Angeles—0
A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks—0
Accounting for Unobservable Heterogeneity in Cross Section Using Spatial First Differences—0
Estimating covariant Lyapunov vectors from data—0
4D iterative reconstruction of brain fMRI in the moving fetus—0
Application of the Non-Hermitian Singular Spectrum Analysis to the exponential retrieval problem—0
Classification of Stochastic Processes with Topological Data Analysis—0
Application of machine learning to gas flaring—0
A Latent Source Model for Nonparametric Time Series Classification—0
Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality—0
Classification of Resting-State fMRI using Evolutionary Algorithms: Towards a Brain Imaging Biomarker for Parkinson's Disease—0
Application of LSTM architectures for next frame forecasting in Sentinel-1 images time series—0
Classification of postoperative surgical site infections from blood measurements with missing data using recurrent neural networks—0
Application of Gaussian Process Regression to Koopman Mode Decomposition for Noisy Dynamic Data—0
Estimación del Exponente de Hurst en Flujos de Tráfico Autosimilares—0
Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting—0
A Langevin model for complex cardiological time series—0
A new hazard event classification model via deep learning and multifractal—0
Classification of Hand Movements from EEG using a Deep Attention-based LSTM Network—0
Application of Deep Interpolation Network for Clustering of Physiologic Time Series—0
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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