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 2076–2100 of 6748 papers

TitleStatusHype
Causal Analysis and Prediction of Human Mobility in the U.S. during the COVID-19 Pandemic—0
Interpreting Machine Learning Models for Room Temperature Prediction in Non-domestic BuildingsCode1
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version—0
Light-weight Gesture Sensing Using FMCW Radar Time Series Data—0
4D iterative reconstruction of brain fMRI in the moving fetus—0
Modeling Irregular Time Series with Continuous Recurrent UnitsCode1
Time Series Prediction about Air Quality using LSTM-Based Models: A Systematic Mapping—0
Learning Non-Stationary Time-Series with Dynamic Pattern Extractions—0
Vehicular Visible Light Communications Noise Analysis and Autoencoder Based Denoising—0
Unsupervised Visual Time-Series Representation Learning and Clustering—0
How News Evolves? Modeling News Text and Coverage using Graphs and Hawkes Process—0
DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behavior—0
A transformer-based model for default prediction in mid-cap corporate markets—0
Causal Forecasting:Generalization Bounds for Autoregressive ModelsCode1
GAETS: A Graph Autoencoder Time Series Approach Towards Battery Parameter Estimation—0
Smart Data Representations: Impact on the Accuracy of Deep Neural NetworksCode0
HiRID-ICU-Benchmark -- A Comprehensive Machine Learning Benchmark on High-resolution ICU DataCode1
Uncertainty-Aware Multiple Instance Learning from Large-Scale Long Time Series Data—0
Online Advertising Revenue Forecasting: An Interpretable Deep Learning Approach—0
Switching Recurrent Kalman Networks—0
Towards Generating Real-World Time Series DataCode1
Graph neural network-based fault diagnosis: a review—0
Outlier Detection as Instance Selection Method for Feature Selection in Time Series Classification—0
A similarity measurement for time series and its application to the stock market—0
Machine Learning for Genomic Data—0
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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