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 1476–1500 of 6748 papers

TitleStatusHype
A Soft Computing Approach for Selecting and Combining Spectral Bands—0
Conditional Loss and Deep Euler Scheme for Time Series Generation—0
A Spatial-Temporal Decomposition Based Deep Neural Network for Time Series Forecasting—0
Conditional-UNet: A Condition-aware Deep Model for Coherent Human Activity Recognition From Wearables—0
Among-site variability in the stochastic dynamics of East African coral reefs—0
Artificial neural network as a universal model of nonlinear dynamical systems—0
ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces—0
A Data-driven Market Simulator for Small Data Environments—0
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps—0
Learning Informative Health Indicators Through Unsupervised Contrastive Learning—0
Contrastive Learning for Time Series on Dynamic Graphs—0
Conditional Risk Minimization for Stochastic Processes—0
Contrastive Learning Is Not Optimal for Quasiperiodic Time Series—0
Contrastive learning of strong-mixing continuous-time stochastic processes—0
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition—0
Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecasting—0
Kernel Hypothesis Testing with Set-valued Data—0
Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data—0
Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning—0
Controlled time series generation for automotive software-in-the-loop testing using GANs—0
Controlling Contents in Data-to-Document Generation with Human-Designed Topic Labels—0
Controlling False Discovery Rates under Cross-Sectional Correlations—0
Convergence of GANs Training: A Game and Stochastic Control Methodology—0
Assessing the effect of advertising expenditures upon sales: a Bayesian structural time series model—0
A Robust Score-Driven Filter for Multivariate Time Series—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