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 3876–3900 of 6748 papers

TitleStatusHype
MTSS-GAN: Multivariate Time Series Simulation Generative Adversarial NetworksCode1
Forecasting Precipitable Water Vapor Using LSTMsCode0
Improving MF-DFA model with applications in precious metals market—0
An Investigation of Traffic Density Changes inside Wuhan during the COVID-19 Epidemic with GF-2 Time-Series Images—0
A GRU-based Mixture Density Network for Data-Driven Dynamic Stochastic Programming—0
Covariance-engaged Classification of Sets via Linear Programming—0
The Signature Kernel is the solution of a Goursat PDECode1
SAR2SAR: a semi-supervised despeckling algorithm for SAR imagesCode1
A Model of the Fed's View on InflationCode0
Combining Ensemble Kalman Filter and Reservoir Computing to predict spatio-temporal chaotic systems from imperfect observations and models—0
Line Spectrum Representation for Vector Processes With Application to Frequency Estimation—0
On Multivariate Singular Spectrum Analysis and its Variants—0
Crop Yield Prediction Integrating Genotype and Weather Variables Using Deep Learning—0
Physics-informed machine learning for sensor fault detection with flight test data—0
Time Series Extrinsic RegressionCode1
Clinical Risk Prediction with Temporal Probabilistic Asymmetric Multi-Task LearningCode1
A Comparative Study of Gamma Markov Chains for Temporal Non-Negative Matrix FactorizationCode0
Aligning Time Series on Incomparable SpacesCode1
Hidden Markov Nonlinear ICA: Unsupervised Learning from Nonstationary Time SeriesCode1
Short-Term Traffic Forecasting Using High-Resolution Traffic Data—0
A Data-driven Market Simulator for Small Data Environments—0
A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic ForecastingCode2
Chaos may enhance expressivity in cerebellar granular layer—0
Frequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence FunctionsCode0
Predicting Temporal Sets with Deep Neural NetworksCode1
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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