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 851–875 of 6748 papers

TitleStatusHype
Attention to Warp: Deep Metric Learning for Multivariate Time SeriesCode1
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial LearningCode1
Attentive Neural Controlled Differential Equations for Time-series Classification and ForecastingCode1
An efficient aggregation method for the symbolic representation of temporal dataCode1
catch22: CAnonical Time-series CHaracteristicsCode1
SMATE: Semi-Supervised Spatio-Temporal Representation Learning on Multivariate Time SeriesCode1
Soft-DTW: a Differentiable Loss Function for Time-SeriesCode1
Soil moisture estimation from Sentinel-1 interferometric observations over arid regionsCode1
Classification of Long Sequential Data using Circular Dilated Convolutional Neural NetworksCode1
Source Model Selection for Deep Learning in the Time Series DomainCode1
Spacecraft Anomaly Detection with Attention Temporal Convolution NetworkCode1
Space-Time-Separable Graph Convolutional Network for Pose ForecastingCode1
Convolutional Radio Modulation Recognition NetworksCode1
Spatiotemporal information conversion machine for time-series predictionCode1
Spatio-Temporal Graph Convolution for Resting-State fMRI AnalysisCode1
Spatio-Temporal Meta-Graph Learning for Traffic ForecastingCode1
An empirical evaluation of attention-based multi-head models for improved turbofan engine remaining useful life predictionCode1
Spatiotemporal Propagation Learning for Network-Wide Flight Delay PredictionCode1
An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence ModelingCode1
Heracles: A Hybrid SSM-Transformer Model for High-Resolution Image and Time-Series AnalysisCode1
StaDRe and StaDRo: Reliability and Robustness Estimation of ML-based Forecasting using Statistical Distance MeasuresCode1
Statistical Perspective on Functional and Causal Neural Connectomics: The Time-Aware PC AlgorithmCode1
Stiff Neural Ordinary Differential EquationsCode1
Stochastic Super-Resolution for Downscaling Time-Evolving Atmospheric Fields with a Generative Adversarial NetworkCode1
Deep Switching State Space Model (DS^3M) for Nonlinear Time Series Forecasting with Regime SwitchingCode1
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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