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 3501–3525 of 6748 papers

TitleStatusHype
Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features—0
Spatial Neural Networks and their Functional Samples: Similarities and Differences—0
Spatial-Temporal Adaptive Graph Convolution with Attention Network for Traffic Forecasting—0
Spatial, Temporal, and Geometric Fusion for Remote Sensing Images—0
Spatial Temporal Graph Convolution with Graph Structure Self-learning for Early MCI Detection—0
Spatial-temporal wind field prediction by Artificial Neural Networks—0
Spatio-Temporal Activation Function To Map Complex Dynamical Systems—0
Spatiotemporal Analysis Using Riemannian Composition of Diffusion Operators—0
Spatiotemporal Cardiac Statistical Shape Modeling: A Data-Driven Approach—0
Spatiotemporal clustering, climate periodicity, and social-ecological risk factors for dengue during an outbreak in Machala, Ecuador, in 2010—0
Spatio-Temporal Functional Neural Networks—0
Spatio-temporal graph neural networks for multi-site PV power forecasting—0
Spatio-Temporal Graph Scattering Transform—0
Spatio-Temporal Graph Structure Learning for Traffic Forecasting—0
Spatio-temporal Manifold Learning for Human Motions via Long-horizon Modeling—0
Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery—0
COVID-19 mortality analysis from soft-data multivariate curve regression and machine learning—0
Spatio-Temporal Representation Learning Enhanced Source Cell-phone Recognition from Speech Recordings—0
Spatiotemporal Representation Learning on Time Series with Dynamic Graph ODEs—0
Spatio-temporal Stacked LSTM for Temperature Prediction in Weather Forecasting—0
Spatiotemporal Tensor Completion for Improved Urban Traffic Imputation—0
Specific low frequency electromagnetic fields induce epigenetic and functional changes in U937 cells—0
Spectral Correlation Hub Screening of Multivariate Time Series—0
Spectral independent component analysis with noise modeling for M/EEG source separation—0
Spectral Propagation Graph Network for Few-shot Time Series Classification—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