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 6626–6650 of 6748 papers

TitleStatusHype
Quality change: norm or exception? Measurement, Analysis and Detection of Quality Change in WikipediaCode0
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series DataCode0
Sinkhorn Divergence of Topological Signature Estimates for Time Series ClassificationCode0
Temporal Pattern Attention for Multivariate Time Series ForecastingCode0
Dataset: Rare Event Classification in Multivariate Time SeriesCode0
Changepoint Detection in Noisy Data Using a Novel Residuals Permutation-Based Method (RESPERM): Benchmarking and Application to Single Trial ERPsCode0
Quantifying Emergent Behavior of Autonomous RobotsCode0
Change point detection for graphical models in the presence of missing valuesCode0
Change of human mobility during COVID-19: A United States case studyCode0
A deep convolutional neural network that is invariant to time rescalingCode0
Multiple species animal movements: network properties, disease dynamic and the impact of targeted control actionsCode0
Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEGCode0
Temporal patterns in insulin needs for Type 1 diabetesCode0
Quantifying the dynamics of topical fluctuations in languageCode0
Challenges in detecting evolutionary forces in language change using diachronic corporaCode0
A Transformer Framework for Data Fusion and Multi-Task Learning in Smart CitiesCode0
Sketches for Time-Dependent Machine LearningCode0
Enhancing Visual Inspection Capability of Multi-Modal Large Language Models on Medical Time Series with Supportive Conformalized and Interpretable Small Specialized ModelsCode0
Enhancing Time Series Momentum Strategies Using Deep Neural NetworksCode0
Using generalized additive models to decompose time series and waveforms, and dissect heart-lung interaction physiologyCode0
Characterizing and Forecasting User Engagement with In-app Action Graph: A Case Study of SnapchatCode0
Patch LearningCode0
Anomaly Detection with Generative Adversarial Networks for Multivariate Time SeriesCode0
Video Trajectory Classification and Anomaly Detection Using Hybrid CNN-VAECode0
Multi-Scale Convolutional Neural Networks for Time Series ClassificationCode0
Show:102550
← PrevPage 266 of 270Next →
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