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 1751–1775 of 6748 papers

TitleStatusHype
DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization—0
Deep Recurrent Modelling of Granger Causality with Latent Confounding—0
Macroeconomic Effect of Uncertainty and Financial Shocks: a non-Gaussian VAR approach—0
Combating Distribution Shift for Accurate Time Series Forecasting via HypernetworksCode1
Learning Dynamics and Structure of Complex Systems Using Graph Neural Networks—0
PyTorch Geometric Signed Directed: A Software Package on Graph Neural Networks for Signed and Directed GraphsCode1
A Deep Learning Model for Forecasting Global Monthly Mean Sea Surface Temperature Anomalies—0
Estimation of Evaporator Valve Sizes in Supermarket Refrigeration Cabinets—0
Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection—0
Integrated Fault Diagnosis and Control Design for DER Inverters using Machine Learning Methods—0
Time Series Analysis of Blockchain-Based Cryptocurrency Price ChangesCode0
A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph Neural Network—0
Long Run Risk in Stationary Structural Vector Autoregressive Models—0
Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting—0
PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting—0
Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models—0
Signal Decomposition Using Masked Proximal OperatorsCode1
"Back to the future" projections for COVID-19 surgesCode0
GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs—0
Multivariate Time Series Forecasting with Dynamic Graph Neural ODEsCode1
Ensemble Conformalized Quantile Regression for Probabilistic Time Series ForecastingCode1
Multi-Objective Model Selection for Time Series Forecasting—0
Level set based particle filter driven by optical flow: an application to track the salt boundary from X-ray CT time-series—0
SAITS: Self-Attention-based Imputation for Time SeriesCode0
Multi-View Fusion Transformer for Sensor-Based Human Activity Recognition—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