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

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Showing 751–775 of 6748 papers

TitleStatusHype
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time SeriesCode1
Aligning Time Series on Incomparable SpacesCode1
Predicting Temporal Sets with Deep Neural NetworksCode1
Efficient implementations of echo state network cross-validationCode1
Monash University, UEA, UCR Time Series Extrinsic Regression ArchiveCode1
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series DataCode1
Low-Rank Autoregressive Tensor Completion for Multivariate Time Series ForecastingCode1
Temporal Phenotyping using Deep Predictive Clustering of Disease ProgressionCode1
COT-GAN: Generating Sequential Data via Causal Optimal TransportCode1
Online Change Point Detection in Molecular Dynamics With Optical Random FeaturesCode1
Inductive Graph Neural Networks for Spatiotemporal KrigingCode1
Reservoir Computing meets Recurrent Kernels and Structured TransformsCode1
Seq2Tens: An Efficient Representation of Sequences by Low-Rank Tensor ProjectionsCode1
A bio-inspired bistable recurrent cell allows for long-lasting memoryCode1
Wavelet Networks: Scale-Translation Equivariant Learning From Raw Time-SeriesCode1
CRISP: A Probabilistic Model for Individual-Level COVID-19 Infection Risk Estimation Based on Contact DataCode1
Cost-effective Interactive Attention Learning with Neural Attention ProcessesCode1
Conditional Sig-Wasserstein GANs for Time Series GenerationCode1
Deep Stock PredictionsCode1
Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and FilteringCode1
Learning Long-Term Dependencies in Irregularly-Sampled Time SeriesCode1
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
Joint learning of variational representations and solvers for inverse problems with partially-observed dataCode1
Interpretable Time-series Classification on Few-shot SamplesCode1
Adaptive Checkpoint Adjoint Method for Gradient Estimation in Neural ODECode1
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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