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 6051–6100 of 6748 papers

TitleStatusHype
Deep Generative Quantile-Copula Models for Probabilistic Forecasting—0
Deep Generative SToRM model for dynamic imaging—0
Deep Generators on Commodity Markets; application to Deep Hedging—0
DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behavior—0
Deep Haar Scattering Networks in Pattern Recognition: A promising approach—0
Deep Hedging, Generative Adversarial Networks, and Beyond—0
Deep Hedging under Rough Volatility—0
Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking—0
Interpretable Deep Representation Learning from Temporal Multi-view Data—0
Deep learning algorithm for data-driven simulation of noisy dynamical system—0
Deep Learning Alternative to Explicit Model Predictive Control for Unknown Nonlinear Systems—0
Deep Learning Approaches for Forecasting Strawberry Yields and Prices Using Satellite Images and Station-Based Soil Parameters—0
Deep Learning Architectures for FSCV, a Comparison—0
Deep Learning based Covert Attack Identification for Industrial Control Systems—0
Deep Learning Based on Generative Adversarial and Convolutional Neural Networks for Financial Time Series Predictions—0
Deep learning-based sequential pattern mining for progressive database—0
Deep learning based sferics recognition for AMT data processing in the dead band—0
Deep Learning-based Time-varying Channel Estimation for RIS Assisted Communication—0
Deep Learning-Based Vehicle Speed Prediction for Ecological Adaptive Cruise Control in Urban and Highway Scenarios—0
Deep learning delay coordinate dynamics for chaotic attractors from partial observable data—0
Deep Learning Enables Reduced Gadolinium Dose for Contrast-Enhanced Blood-Brain Barrier Opening—0
Deep Learning for Energy Markets—0
Deep Learning for Energy Time-Series Analysis and Forecasting—0
Deep Learning for Epidemiological Predictions—0
Deep Learning for Multi-Scale Changepoint Detection in Multivariate Time Series—0
Deep Learning for Plasma Tomography and Disruption Prediction from Bolometer Data—0
Deep learning for ψ-weakly dependent processes—0
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data—0
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation: Results with Advanced LIGO Data—0
Deep Learning for Satellite Image Time Series Analysis: A Review—0
Deep Learning for Stock Selection Based on High Frequency Price-Volume Data—0
Deep learning for structural health monitoring: An application to heritage structures—0
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies—0
Deep Learning for Time-Series Analysis—0
Deep Learning in Asset Pricing—0
Deep Learning in Multiple Multistep Time Series Prediction—0
Deep-Learning Inversion of Seismic Data—0
Deep learning of contagion dynamics on complex networks—0
Deep Learning, Predictability, and Optimal Portfolio Returns—0
Deep Learning to Attend to Risk in ICU—0
On model selection for scalable time series forecasting in transport networks—0
Deep Learning with Attention Mechanism for Predicting Driver Intention at Intersection—0
Deep Learning with Convolutional Neural Network for Objective Skill Evaluation in Robot-assisted Surgery—0
Deep Learning with Kernel Flow Regularization for Time Series Forecasting—0
Deep Learning with Long Short-Term Memory for Time Series Prediction—0
Deep Metric Learning with Locality Sensitive Angular Loss for Self-Correcting Source Separation of Neural Spiking Signals—0
DeepMoTIon: Learning to Navigate Like Humans—0
Deep MR Fingerprinting with total-variation and low-rank subspace priors—0
Deep Multimodal Learning: An Effective Method for Video Classification—0
Deep Multimodal Representation Learning from Temporal Data—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