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 3601–3625 of 6748 papers

TitleStatusHype
A deep network approach to multitemporal cloud detection—0
Automatic Registration and Clustering of Time Series—0
Topology Identification under Spatially Correlated Noise—0
An Expectation-Based Network Scan Statistic for a COVID-19 Early Warning System—0
An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks—0
In-N-Out: Pre-Training and Self-Training using Auxiliary Information for Out-of-Distribution RobustnessCode0
An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth—0
Dynamic Clustering in Federated Learning—0
AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous SensorsCode0
Randomized kernels for large scale Earth observation applications—0
A Novel Hybrid Framework for Hourly PM2.5 Concentration Forecasting Using CEEMDAN and Deep Temporal Convolutional Neural Network—0
Physics-Aware Gaussian Processes in Remote Sensing—0
Spatio-Temporal Graph Scattering Transform—0
Modified Auto Regressive Technique for Univariate Time Series Prediction of Solar Irradiance—0
Estimating Vector Fields from Noisy Time SeriesCode0
[Re] Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-AttentionCode0
Learn to Predict Vertical Track Irregularity with Extremely Imbalanced Data—0
Fusing Optical and SAR time series for LAI gap filling with multioutput Gaussian processes—0
LandCoverNet: A global benchmark land cover classification training datasetCode0
Learning summary features of time series for likelihood free inference—0
Inference in mixed causal and noncausal models with generalized Student's t-distributions—0
Benchmarking Energy-Conserving Neural Networks for Learning Dynamics from Data—0
Concept-based model explanations for Electronic Health RecordsCode0
Forecast with Forecasts: Diversity Matters—0
SAFCAR: Structured Attention Fusion for Compositional Action Recognition—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