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 1076–1100 of 6748 papers

TitleStatusHype
Informative regularization for a multi-layer perceptron RR Lyrae classifier under data shiftCode0
A Dual-Stage Attention-Based Recurrent Neural Network for Time Series PredictionCode0
Augmenting Neural Differential Equations to Model Unknown Dynamical Systems with Incomplete State InformationCode0
Highly Scalable and Provably Accurate Classification in Poincare BallsCode0
An Efficient Method for the Classification of Croplands in Scarce-Label RegionsCode0
A Two-stream Convolutional Network for Musculoskeletal and Neurological Disorders PredictionCode0
High dimensional regression for regenerative time-series: an application to road traffic modelingCode0
High-dimensional regression with potential prior information on variable importanceCode0
HigeNet: A Highly Efficient Modeling for Long Sequence Time Series Prediction in AIOpsCode0
Hierarchical Probabilistic Model for Blind Source Separation via Legendre TransformationCode0
High-Dimensional Multivariate Forecasting with Low-Rank Gaussian Copula ProcessesCode0
Hide-and-Seek Privacy ChallengeCode0
Hidden Parameter Recurrent State Space Models For Changing Dynamics ScenariosCode0
Hierarchical Attention-Based Recurrent Highway Networks for Time Series PredictionCode0
HERMES: Hybrid Error-corrector Model with inclusion of External Signals for nonstationary fashion time seriesCode0
HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk PredictionCode0
Homological Time Series Analysis of Sensor Signals from Power PlantsCode0
An attention model to analyse the risk of agitation and urinary tract infections in people with dementiaCode0
Guidelines for Augmentation Selection in Contrastive Learning for Time Series ClassificationCode0
Guiding Sentiment Analysis with Hierarchical Text Clustering: Analyzing the German X/Twitter Discourse on Face Masks in the 2020 COVID-19 PandemicCode0
Attending to Emotional NarrativesCode0
Time-Series Event Prediction with Evolutionary State GraphCode0
Attaining entropy production and dissipation maps from Brownian movies via neural networksCode0
GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge AggregationCode0
Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal frameworkCode0
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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