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 1626–1650 of 6748 papers

TitleStatusHype
A Deep Structural Model for Analyzing Correlated Multivariate Time Series—0
Data Curves Clustering Using Common Patterns Detection—0
Data-driven Air Quality Characterisation for Urban Environments: a Case Study—0
Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems—0
Data-Driven Construction of Data Center Graph of Things for Anomaly Detection—0
A Model Combining Convolutional Neural Network and LightGBM Algorithm for Ultra-Short-Term Wind Power Forecasting—0
Deep Learning for Plasma Tomography and Disruption Prediction from Bolometer Data—0
Data-Driven Failure Prediction in Brittle Materials: A Phase-Field Based Machine Learning Framework—0
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks—0
Data-driven forecasting of solar irradiance—0
Data-Driven Forecast of Dengue Outbreaks in Brazil: A Critical Assessment of Climate Conditions for Different Capitals—0
Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators—0
Data-driven Influence Based Clustering of Dynamical Systems—0
Data-Driven Interaction Analysis of Line Failure Cascading in Power Grid Networks—0
Data-Driven Learning of the Number of States in Multi-State Autoregressive Models—0
Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System—0
Data-driven mapping between functional connectomes using optimal transport—0
Deep Learning for Real-time Gravitational Wave Detection and Parameter Estimation with LIGO Data—0
Data-driven Neural Architecture Learning For Financial Time-series Forecasting—0
Deep learning for structural health monitoring: An application to heritage structures—0
Data-Driven Fault Diagnosis Analysis and Open-Set Classification of Time-Series Data—0
Deep-Learning Inversion of Seismic Data—0
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps—0
Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models—0
DeepMoTIon: Learning to Navigate Like Humans—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