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

Showing 16761700 of 6748 papers

TitleStatusHype
Real-time Power System State Estimation and Forecasting via Deep Neural NetworksCode0
Efficient Matrix Profile Computation Using Different Distance FunctionsCode0
Real Time Trajectory Prediction Using Deep Conditional Generative ModelsCode0
Attaining entropy production and dissipation maps from Brownian movies via neural networksCode0
RecovDB: accurate and efficient missing blocks recovery for large time seriesCode0
Time-Series Event Prediction with Evolutionary State GraphCode0
Quantum open system identification via global optimization: Optimally accurate Markovian models of open systems from time-series dataCode0
Efficient Certified Training and Robustness Verification of Neural ODEsCode0
Deep Efficient Continuous Manifold Learning for Time Series ModelingCode0
Recurrent Auto-Encoder Model for Large-Scale Industrial Sensor Signal AnalysisCode0
Efficient Covariance Estimation from Temporal DataCode0
EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous VariablesCode0
Caulking the Leakage Effect in MEEG Source Connectivity AnalysisCode0
EasyMLServe: Easy Deployment of REST Machine Learning ServicesCode0
Economy Statistical Recurrent Units For Inferring Nonlinear Granger CausalityCode0
Early Abandoning PrunedDTW and its application to similarity searchCode0
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health EpisodesCode0
An attention model to analyse the risk of agitation and urinary tract infections in people with dementiaCode0
Edge computing on TPU for brain implant signal analysisCode0
Dynamic transformation of prior knowledge into Bayesian models for data streamsCode0
Dynamic Virtual Graph Significance Networks for Predicting InfluenzaCode0
A novel approach to rating transition modelling via Machine Learning and SDEs on Lie groupsCode0
MASA: Motif-Aware State Assignment in Noisy Time Series DataCode0
Reinforcement Learning for Portfolio ManagementCode0
Dynamic Time Warping based Adversarial Framework for Time-Series DomainCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified