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 4651–4675 of 6748 papers

TitleStatusHype
Set Functions for Time SeriesCode0
Data Smashing 2.0: Sequence Likelihood (SL) Divergence For Fast Time Series Comparison—0
The Ant Swarm Neuro-Evolution Procedure for Optimizing Recurrent Networks—0
COMBINED FLEXIBLE ACTIVATION FUNCTIONS FOR DEEP NEURAL NETWORKS—0
Adversarially learned anomaly detection for time series data—0
MANIFOLD FORESTS: CLOSING THE GAP ON NEURAL NETWORKS—0
RISE and DISE: Two Frameworks for Learning from Time Series with Missing Data—0
Continuous Convolutional Neural Network forNonuniform Time Series—0
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning—0
Recurrent Neural Networks are Universal Filters—0
CGT: Clustered Graph Transformer for Urban Spatio-temporal Prediction—0
Temporal Probabilistic Asymmetric Multi-task Learning—0
Detecting Change in Seasonal Pattern via Autoencoder and Temporal Regularization—0
Granger Causal Structure Reconstruction from Heterogeneous Multivariate Time Series—0
UNIVERSAL MODAL EMBEDDING OF DYNAMICS IN VIDEOS AND ITS APPLICATIONS—0
Learning Through Limited Self-Supervision: Improving Time-Series Classification Without Additional Data via Auxiliary Tasks—0
Variational pSOM: Deep Probabilistic Clustering with Self-Organizing Maps—0
Actor-Critic Approach for Temporal Predictive Clustering—0
InfoCNF: Efficient Conditional Continuous Normalizing Flow Using Adaptive Solvers—0
Explaining Time Series by Counterfactuals—0
Hierarchical Probabilistic Model for Blind Source Separation via Legendre TransformationCode0
The Dynamical Gaussian Process Latent Variable Model in the Longitudinal Scenario—0
Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep NetworksCode0
WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time SeriesCode0
Interpretable Models for Understanding Immersive Simulations—0
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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