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 5801–5825 of 6748 papers

TitleStatusHype
Probabilistic AutoRegressive Neural Networks for Accurate Long-range ForecastingCode0
Visually Evaluating Generative Adversarial Networks Using Itself under Multivariate Time SeriesCode0
Non-iterative Calculation of Quasi-Dynamic Energy Flow in the Heat and Electricity Integrated Energy SystemsCode0
Data augmentation through multivariate scenario forecasting in Data Centers using Generative Adversarial NetworksCode0
Bridging Simplicity and Sophistication using GLinear: A Novel Architecture for Enhanced Time Series PredictionCode0
Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image ObservationsCode0
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive LearningCode0
Integrating Attention Feedback into the Recurrent Neural NetworkCode0
Nonlinear Independent Component Analysis for Discrete-Time and Continuous-Time SignalsCode0
Identifying cross country skiing techniques using power meters in ski polesCode0
Recurrent Neural Networks for Time Series Forecasting: Current Status and Future DirectionsCode0
Recurrent Neural ProcessesCode0
Intelligence, physics and information -- the tradeoff between accuracy and simplicity in machine learningCode0
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the PastCode0
Inter- and Intra-Series Embeddings Fusion Network for Epidemiological ForecastingCode0
Nonlinear methods to quantify Movement Variability in Human-Humanoid Interaction ActivitiesCode0
Distribution Agnostic Symbolic Representations for Time Series Dimensionality Reduction and Online Anomaly DetectionCode0
Interpolation-Prediction Networks for Irregularly Sampled Time SeriesCode0
Recurrent switching linear dynamical systemsCode0
Data augmentation on-the-fly and active learning in data stream classificationCode0
Nonlinear Time Series Classification Using Bispectrum-based Deep Convolutional Neural NetworksCode0
An Interpretable and Sparse Neural Network Model for Nonlinear Granger Causality DiscoveryCode0
Recursive classification of satellite imaging time-series: An application to land cover mappingCode0
Recursive deep learning framework for forecasting the decadal world economic outlookCode0
BreizhCrops: A Time Series Dataset for Crop Type MappingCode0
Show:102550
← PrevPage 233 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