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 4101–4125 of 6748 papers

TitleStatusHype
Stanza: A Nonlinear State Space Model for Probabilistic Inference in Non-Stationary Time Series—0
Model-Size Reduction for Reservoir Computing by Concatenating Internal States Through Time—0
Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents—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
Distribution Regression for Sequential Data—0
Hierarchical regularization networks for sparsification based learning on noisy datasets—0
Deep learning of contagion dynamics on complex networks—0
Sparse Dynamic Distribution Decomposition: Efficient Integration of Trajectory and Snapshot Time Series DataCode0
Statistical Estimation of High-Dimensional Vector Autoregressive Models—0
Detecting structural perturbations from time series with deep learningCode0
tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder—0
Dynamic Time Warping as a New Evaluation for Dst Forecast with Machine LearningCode0
EnK: Encoding time-information in convolutionCode0
Hybrid Model for Anomaly Detection on Call Detail Records by Time Series Forecasting—0
Online learning of both state and dynamics using ensemble Kalman filters—0
A precise machine learning aided algorithm for land subsidence or upheave prediction from GNSS time series—0
Multivariate Functional Singular Spectrum Analysis Over Different Dimensional Domains—0
Dimensionless Anomaly Detection on Multivariate Streams with Variance Norm and Path SignatureCode0
Time Series Analysis and Forecasting of COVID-19 Cases Using LSTM and ARIMA Models—0
Neuropsychiatric Disease Classification Using Functional Connectomics -- Results of the Connectomics in NeuroImaging Transfer Learning Challenge—0
Uncovering the mesoscale structure of the credit default swap market to improve portfolio risk modelling—0
Fast CRDNN: Towards on Site Training of Mobile Construction Machines—0
A New Look to Three-Factor Fama-French Regression Model using Sample Innovations—0
AdaVol: An Adaptive Recursive Volatility Prediction MethodCode0
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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