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 326–350 of 6748 papers

TitleStatusHype
Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting EpidemicsCode1
Scalable Classifier-Agnostic Channel Selection for Multivariate Time Series ClassificationCode1
A Deep Learning Approach for the Segmentation of Electroencephalography Data in Eye Tracking ApplicationsCode1
StaDRe and StaDRo: Reliability and Robustness Estimation of ML-based Forecasting using Statistical Distance MeasuresCode1
MAGIC: Microlensing Analysis Guided by Intelligent ComputationCode1
Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential EquationsCode1
Closed-Form Diffeomorphic Transformations for Time Series AlignmentCode1
When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series ForecastingCode1
Conformal prediction set for time-seriesCode1
Deep Isolation Forest for Anomaly DetectionCode1
Efficient recurrent architectures through activity sparsity and sparse back-propagation through timeCode1
Contrastive Learning for Unsupervised Domain Adaptation of Time SeriesCode1
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformersCode1
Scaleformer: Iterative Multi-scale Refining Transformers for Time Series ForecastingCode1
Motiflets -- Simple and Accurate Detection of Motifs in Time SeriesCode1
VitalDBCode1
Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamicsCode1
TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning (with Appendices on Detailed Network Architectures and Experimental Cases of Study)Code1
Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning RulesCode1
SOM-CPC: Unsupervised Contrastive Learning with Self-Organizing Maps for Structured Representations of High-Rate Time SeriesCode1
Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN)Code1
Sparse Graph Learning from Spatiotemporal Time SeriesCode1
Learning the spatio-temporal relationship between wind and significant wave height using deep learningCode1
Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse ObservationsCode1
FBNETGEN: Task-aware GNN-based fMRI Analysis via Functional Brain Network GenerationCode1
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All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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