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 1726–1750 of 6748 papers

TitleStatusHype
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series PredictionCode0
MASA: Motif-Aware State Assignment in Noisy Time Series DataCode0
Efficient Covariance Estimation from Temporal DataCode0
Elastic bands across the path: A new framework and methods to lower bound DTWCode0
Economy Statistical Recurrent Units For Inferring Nonlinear Granger CausalityCode0
Capturing the temporal constraints of gradual patternsCode0
Edge computing on TPU for brain implant signal analysisCode0
Capturing Structure Implicitly from Time-Series having Limited DataCode0
Capturing Actionable Dynamics with Structured Latent Ordinary Differential EquationsCode0
Early Anomaly Detection in Time Series: A Hierarchical Approach for Predicting Critical Health EpisodesCode0
Early Abandoning PrunedDTW and its application to similarity searchCode0
EasyMLServe: Easy Deployment of REST Machine Learning ServicesCode0
Efficient Certified Training and Robustness Verification of Neural ODEsCode0
Dynamic Time Warping based Adversarial Framework for Time-Series DomainCode0
A New Valuation Measure for the Stock MarketCode0
Dynamic transformation of prior knowledge into Bayesian models for data streamsCode0
Dynamic Time Warping as a New Evaluation for Dst Forecast with Machine LearningCode0
Dynamic Virtual Graph Significance Networks for Predicting InfluenzaCode0
Dynamic process fault prediction using canonical variable trend analysisCode0
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time seriesCode0
Semi-Supervised Recurrent Variational Autoencoder Approach for Visual Diagnosis of Atrial FibrillationCode0
Dynamic Natural Language Processing with Recurrence Quantification AnalysisCode0
Deep Efficient Continuous Manifold Learning for Time Series ModelingCode0
Dynamic cyber risk estimation with Competitive Quantile AutoregressionCode0
DynaConF: Dynamic Forecasting of Non-Stationary Time SeriesCode0
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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