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 4576–4600 of 6748 papers

TitleStatusHype
Learning Non-Stationary Time-Series with Dynamic Pattern Extractions—0
Learning Over Long Time Lags—0
Learning Partially Known Stochastic Dynamics with Empirical PAC Bayes—0
Learning PDE Solution Operator for Continuous Modeling of Time-Series—0
Learning Predictive and Interpretable Timeseries Summaries from ICU Data—0
Learning Probabilistic Intersection Traffic Models for Trajectory Prediction—0
Learning Probability Distributions in Macroeconomics and Finance—0
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting—0
Learning Quantities of Interest from Dynamical Systems for Observation-Consistent Inversion—0
Learning Representation for Anomaly Detection of Vehicle Trajectories—0
Learning Representations for Incomplete Time Series Clustering—0
Learning representations for multivariate time series with missing data using Temporal Kernelized Autoencoders—0
Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection—0
Learning Representations from Imperfect Time Series Data via Tensor Rank Regularization—0
Learning Representations of Missing Data for Predicting Patient Outcomes—0
Learning Representations Using Complex-Valued Nets—0
Learning Representative Temporal Features for Action Recognition—0
Learning Reservoir Dynamics with Temporal Self-Modulation—0
Learning Sentinel-2 Spectral Dynamics for Long-Run Predictions Using Residual Neural Networks—0
Learning State Transition Rules from Hidden Layers of Restricted Boltzmann Machines—0
Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels—0
Learning summary features of time series for likelihood free inference—0
Learning Temporal Dependence from Time-Series Data with Latent Variables—0
Learning temporal evolution of probability distribution with Recurrent Neural Network—0
Learning the Conditional Independence Structure of Stationary Time Series: A Multitask Learning Approach—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