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 6326–6350 of 6748 papers

TitleStatusHype
Manifold-regression to predict from MEG/EEG brain signals without source modelingCode0
Fast Online Deconvolution of Calcium Imaging DataCode0
Fast Gaussian Process Based Gradient Matching for Parameter Identification in Systems of Nonlinear ODEsCode0
Switching Autoregressive Low-rank Tensor ModelsCode0
A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time SeriesCode0
Pooled Motion Features for First-Person VideosCode0
POP: Mining POtential Performance of new fashion products via webly cross-modal query expansionCode0
Self-explaining Hierarchical Model for Intraoperative Time SeriesCode0
Automatic alignment of surgical videos using kinematic dataCode0
AdaVol: An Adaptive Recursive Volatility Prediction MethodCode0
Approximating Continuous Functions on Persistence Diagrams Using Template FunctionsCode0
Fast fitting of neural ordinary differential equations by Bayesian neural gradient matching to infer ecological interactions from time series dataCode0
Fast ES-RNN: A GPU Implementation of the ES-RNN AlgorithmCode0
Conditional Generation of Medical Time Series for Extrapolation to Underrepresented PopulationsCode0
Deep Neural Network Ensembles for Time Series ClassificationCode0
Conditional Approximate Normalizing Flows for Joint Multi-Step Probabilistic Forecasting with Application to Electricity DemandCode0
Concept-based model explanations for Electronic Health RecordsCode0
Deep Multi-View Spatial-Temporal Network for Taxi Demand PredictionCode0
An accuracy-runtime trade-off comparison of scalable Gaussian process approximations for spatial dataCode0
A clustering approach to time series forecasting using neural networks: A comparative study on distance-based vs. feature-based clustering methodsCode0
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random FeaturesCode0
Approximate Bayesian Computation with Path SignaturesCode0
Faster Retrieval with a Two-Pass Dynamic-Time-Warping Lower BoundCode0
VELC: A New Variational AutoEncoder Based Model for Time Series Anomaly DetectionCode0
Self-supervised representation learning from electroencephalography signalsCode0
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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