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 5601–5625 of 6748 papers

TitleStatusHype
Recovery of surfaces and functions in high dimensions: sampling theory and links to neural networks—0
RECOWNs: Probabilistic Circuits for Trustworthy Time Series Forecasting—0
Recurrence measures and transitions in stock market dynamics—0
Recurrent Auto-Encoder Model for Multidimensional Time Series Representation—0
Recurrent Auto-Encoder With Multi-Resolution Ensemble and Predictive Coding for Multivariate Time-Series Anomaly Detection—0
Recurrent convolutional neural network for the surrogate modeling of subsurface flow simulation—0
Recurrent Deep Divergence-based Clustering for simultaneous feature learning and clustering of variable length time series—0
Recurrent Graph Tensor Networks: A Low-Complexity Framework for Modelling High-Dimensional Multi-Way Sequence—0
Recurrent LSTM-based UAV Trajectory Prediction with ADS-B Information—0
Recurrent Neural Network Architecture based on Dynamic Systems Theory for Data Driven Modelling of Complex Physical Systems—0
Recurrent Neural Network Based Modeling of Gene Regulatory Network Using Bat Algorithm—0
Recurrent Neural Networks and Universal Approximation of Bayesian Filters—0
Recurrent Neural Networks: An Embedded Computing Perspective—0
Recurrent Neural Networks are Universal Filters—0
Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets—0
Recurrent Neural Networks for Dynamical Systems: Applications to Ordinary Differential Equations, Collective Motion, and Hydrological Modeling—0
Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study—0
Recurrent Neural Networks for Time Series Forecasting—0
Recurrent-type Neural Networks for Real-time Short-term Prediction of Ship Motions in High Sea State—0
Viking: Variational Bayesian Variance Tracking—0
Recursive Gaussian Process over graphs for Integrating Multi-timescale Measurements in Low-Observable Distribution Systems—0
Recursive input and state estimation: A general framework for learning from time series with missing data—0
Recursive Least Squares Policy Control with Echo State Network—0
Recursive Sparse Point Process Regression with Application to Spectrotemporal Receptive Field Plasticity Analysis—0
Redes Generativas Adversarias (GAN) Fundamentos Teóricos y Aplicaciones—0
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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