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 3426–3450 of 6748 papers

TitleStatusHype
Signal Transformer: Complex-valued Attention and Meta-Learning for Signal Recognition—0
Signatures Meet Dynamic Programming: Generalizing Bellman Equations for Trajectory Following—0
Signatures of brain criticality unveiled by maximum entropy analysis across cortical states—0
Signs in time: Encoding human motion as a temporal image—0
Sig-SDEs model for quantitative finance—0
Silicon Photonic Microring Based Chip-Scale Accelerator for Delayed Feedback Reservoir Computing—0
SILVar: Single Index Latent Variable Models—0
Similarity Grouping-Guided Neural Network Modeling for Maritime Time Series Prediction—0
Similarity Learning for Time Series Classification—0
Similarity measure for Public Persons—0
Similarity Preserving Representation Learning for Time Series Clustering—0
Simple Models and Biased Forecasts—0
Simple Yet Surprisingly Effective Training Strategies for LSTMs in Sensor-Based Human Activity Recognition—0
Simplicial persistence of financial markets: filtering, generative processes and portfolio risk—0
Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings—0
Simulating extrapolated dynamics with parameterization networks—0
Simulating financial time series using attention—0
Simulating Network Paths with Recurrent Buffering Units—0
Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models—0
Simulating Using Deep Learning The World Trade Forecasting of Export-Import Exchange Rate Convergence Factor During COVID-19—0
Simultaneous Implementation Features Extraction and Recognition Using C3D Network for WiFi-based Human Activity Recognition—0
Simultaneous Multivariate Forecast of Space Weather Indices using Deep Neural Network Ensembles—0
Sinkhorn-Flow: Predicting Probability Mass Flow in Dynamical Systems Using Optimal Transport—0
Size-Consistent Statistics for Anomaly Detection in Dynamic Networks—0
Skeletonnet: Mining deep part features for 3-d action recognition—0
Show:102550
← PrevPage 138 of 270Next →
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