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 4326–4350 of 6748 papers

TitleStatusHype
Inference in heavy-tailed non-stationary multivariate time series—0
Inference in mixed causal and noncausal models with generalized Student's t-distributions—0
Inference in Non-stationary High-Dimensional VARs—0
Inference of Binary Regime Models with Jump Discontinuities—0
Inference of Causal Information Flow in Collective Animal Behavior—0
Inference of High-dimensional Autoregressive Generalized Linear Models—0
Inference of stochastic time series with missing data—0
Inference on Causal Effects of Interventions in Time using Gaussian Processes—0
Inference on the change point in high dimensional time series models via plug in least squares—0
Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves—0
INFERENCE, PREDICTION, AND ENTROPY RATE OF CONTINUOUS-TIME, DISCRETE-EVENT PROCESSES—0
Inferential Theory for Granular Instrumental Variables in High Dimensions—0
Inferring Black Hole Properties from Astronomical Multivariate Time Series with Bayesian Attentive Neural Processes—0
Inferring, comparing and exploring ecological networks from time-series data through R packages constructnet, disgraph and dynet—0
Identifying nonlinear dynamical systems with multiple time scales and long-range dependencies—0
Inferring extended summary causal graphs from observational time series—0
Inferring Global Dynamics Using a Learning Machine—0
Causal Graph Discovery from Self and Mutually Exciting Time Series—0
Inferring Individual Level Causal Models from Graph-based Relational Time Series—0
Inferring linear and nonlinear Interaction networks using neighborhood support vector machines—0
Inferring Temporal Logic Properties from Data using Boosted Decision Trees—0
Inferring the time-varying functional connectivity of large-scale computer networks from emitted events—0
Infinite-Dimensional Adaptive Boundary Observer for Inner-Domain Temperature Estimation of 3D Electrosurgical Processes using Surface Thermography Sensing—0
Infinite Mixture Model of Markov Chains—0
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes—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