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 2401–2425 of 6748 papers

TitleStatusHype
Estimating Task Completion Times for Network Rollouts using Statistical Models within Partitioning-based Regression Methods—0
Estimating the Algorithmic Complexity of Stock Markets—0
Estimating the causal effect of an intervention in a time series setting: the C-ARIMA approach—0
Change of persistence in European electricity spot prices—0
Estimating Time-varying Brain Connectivity Networks from Functional MRI Time Series—0
Estimating Treatment Effects in Continuous Time with Hidden Confounders—0
A Novel Multi-Stage Training Approach for Human Activity Recognition from Multimodal Wearable Sensor Data Using Deep Neural Network—0
Estimating value at risk: LSTM vs. GARCH—0
Anticipating synchronization with machine learning—0
Causal Triple Attention Time Series Forecasting—0
Estimation and Quantization of Expected Persistence Diagrams—0
A Hybrid Distribution Feeder Long-Term Load Forecasting Method Based on Sequence Prediction—0
Ensemble of Hankel Matrices for Face Emotion Recognition—0
Estimation of Cross-Sectional Dependence in Large Panels—0
Estimation of Evaporator Valve Sizes in Supermarket Refrigeration Cabinets—0
Estimation of High-Dimensional Markov-Switching VAR Models with an Approximate EM Algorithm—0
Change Point Detection via Multivariate Singular Spectrum Analysis—0
Estimation of multivariate asymmetric power GARCH models—0
EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid—0
Estimation of Shade Losses in Unlabeled PV Data—0
Channel-Based Attention for LCC Using Sentinel-2 Time Series—0
Estimation of the mortality rate functions from time series field data in a stage-structured demographic model for Lobesia botrana—0
Ethereum Price Prediction Employing Large Language Models for Short-term and Few-shot Forecasting—0
Extending Deep Learning Models for Limit Order Books to Quantile Regression—0
Extract Dynamic Information To Improve Time Series Modeling: a Case Study with Scientific Workflow—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