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 2301–2325 of 6748 papers

TitleStatusHype
NetRCA: An Effective Network Fault Cause Localization Algorithm—0
NeuroView-RNN: It's About Time—0
Amortised Likelihood-free Inference for Expensive Time-series Simulators with Signatured Ratio Estimation—0
Simulating Network Paths with Recurrent Buffering Units—0
Nowcasting the Financial Time Series with Streaming Data Analytics under Apache Spark—0
DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization—0
Macroeconomic Effect of Uncertainty and Financial Shocks: a non-Gaussian VAR approach—0
Learning Dynamics and Structure of Complex Systems Using Graph Neural Networks—0
Integrated Fault Diagnosis and Control Design for DER Inverters using Machine Learning Methods—0
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
A Novel Anomaly Detection Method for Multimodal WSN Data Flow via a Dynamic Graph Neural Network—0
Time Series Analysis of Blockchain-Based Cryptocurrency Price ChangesCode0
Long Run Risk in Stationary Structural Vector Autoregressive Models—0
PGCN: Progressive Graph Convolutional Networks for Spatial-Temporal Traffic Forecasting—0
Simulating User-Level Twitter Activity with XGBoost and Probabilistic Hybrid Models—0
Dynamic Relation Discovery and Utilization in Multi-Entity Time Series Forecasting—0
"Back to the future" projections for COVID-19 surgesCode0
Multi-Objective Model Selection for Time Series Forecasting—0
GRAPHSHAP: Explaining Identity-Aware Graph Classifiers Through the Language of Motifs—0
SAITS: Self-Attention-based Imputation for Time SeriesCode0
Level set based particle filter driven by optical flow: an application to track the salt boundary from X-ray CT time-series—0
TimeREISE: Time-series Randomized Evolving Input Sample Explanation—0
Auxiliary Cross-Modal Representation Learning with Triplet Loss Functions for Online Handwriting 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