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 3926–3950 of 6748 papers

TitleStatusHype
Online Change Point Detection in Molecular Dynamics With Optical Random FeaturesCode1
COT-GAN: Generating Sequential Data via Causal Optimal TransportCode1
Lateral land movement prediction from GNSS position time series in a machine learning aided algorithm—0
Dynamic Window-level Granger Causality of Multi-channel Time Series—0
Tempered Stable Processes with Time Varying Exponential Tails—0
Inductive Graph Neural Networks for Spatiotemporal KrigingCode1
Interpretable Super-Resolution via a Learned Time-Series Representation—0
FedGAN: Federated Generative Adversarial Networks for Distributed Data—0
Fairness in Forecasting and Learning Linear Dynamical Systems—0
Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor ProjectionsCode1
Reservoir Computing meets Recurrent Kernels and Structured TransformsCode1
Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting—0
Scoring and Assessment in Medical VR Training Simulators with Dynamic Time Series Classification—0
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South AfricaCode0
Model-Size Reduction for Reservoir Computing by Concatenating Internal States Through Time—0
Learning Continuous-Time Dynamics by Stochastic Differential Networks—0
Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series—0
Superconducting radio-frequency cavity fault classification using machine learning at Jefferson Laboratory—0
Entanglement-Embedded Recurrent Network Architecture: Tensorized Latent State Propagation and Chaos Forecasting—0
Deep Learning with Attention Mechanism for Predicting Driver Intention at Intersection—0
Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents—0
Distribution Regression for Sequential Data—0
Conditional Sig-Wasserstein GANs for Time Series GenerationCode1
Statistical Estimation of High-Dimensional Vector Autoregressive Models—0
Cost-effective Interactive Attention Learning with Neural Attention ProcessesCode1
Show:102550
← PrevPage 158 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