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

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Showing 4526–4550 of 6748 papers

TitleStatusHype
Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in HealthcareCode0
SMART: Skeletal Motion Action Recognition aTtack—0
Deep learning for clustering of multivariate clinical patient trajectories with missing valuesCode0
Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China—0
Bayesian nonparametric discontinuity designCode0
A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis—0
Modelling EHR timeseries by restricting feature interaction—0
Synthetic Event Time Series Health Data Generation—0
Robust Parameter-Free Season Length Detection in Time SeriesCode0
Performance evaluation of deep neural networks for forecasting time-series with multiple structural breaks and high volatilityCode0
Real-Time Anomaly Detection for Advanced Manufacturing: Improving on Twitter's State of the Art—0
Self-supervised representation learning from electroencephalography signalsCode0
Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning—0
Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation NetworksCode0
Generating an Explainable ECG Beat Space With Variational Auto-Encoders—0
Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural NetworksCode0
Building Effective Large-Scale Traffic State Prediction System: Traffic4cast Challenge SolutionCode0
Making Good on LSTMs' Unfulfilled Promise—0
Time2Graph: Revisiting Time Series Modeling with Dynamic ShapeletsCode0
Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP EventsCode0
SeismoGen: Seismic Waveform Synthesis Using Generative Adversarial Networks—0
Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs—0
DeVLearn: A Deep Visual Learning Framework for Localizing Temporary Faults in Power Systems—0
XceptionTime: A Novel Deep Architecture based on Depthwise Separable Convolutions for Hand Gesture ClassificationCode0
Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems—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