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 6376–6400 of 6748 papers

TitleStatusHype
Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial NetworksCode0
Variationally Inferred Sampling Through a Refined Bound for Probabilistic ProgramsCode0
Predicting Customer Churn: Extreme Gradient Boosting with Temporal DataCode0
Applicability and interpretation of the deterministic weighted cepstral distanceCode0
Factor-Driven Two-Regime RegressionCode0
Multi-task Meta Label Correction for Time Series PredictionCode0
Semi-Supervised Recurrent Variational Autoencoder Approach for Visual Diagnosis of Atrial FibrillationCode0
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South AfricaCode0
Urban Traffic Prediction from Spatio-Temporal Data Using Deep Meta LearningCode0
Predicting Flat-Fading Channels via Meta-Learned Closed-Form Linear Filters and Equilibrium PropagationCode0
Predicting Future Mosquito Larval Habitats Using Time Series Climate Forecasting and Deep LearningCode0
Comparison of Deep learning models on time series forecasting : a case study of Dissolved Oxygen PredictionCode0
Factor-augmented tree ensemblesCode0
Meta-path Analysis on Spatio-Temporal Graphs for Pedestrian Trajectory PredictionCode0
DMS, AE, DAA: methods and applications of adaptive time series model selection, ensemble, and financial evaluationCode0
Deep-Learnt Classification of Light CurvesCode0
Meteorological indicators of dengue epidemics in non-endemic Northwest ArgentinaCode0
Semi-unsupervised Learning for Time Series ClassificationCode0
Deep Learning MacroeconomicsCode0
Semi-unsupervised Learning of Human Activity using Deep Generative ModelsCode0
Predicting Landsat Reflectance with Deep Generative FusionCode0
Extracting Relationships by Multi-Domain MatchingCode0
Comparing Temporal Graphs Using Dynamic Time WarpingCode0
T-Phenotype: Discovering Phenotypes of Predictive Temporal Patterns in Disease ProgressionCode0
Systematic Generalization in Neural Networks-based Multivariate Time Series Forecasting ModelsCode0
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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