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 976–1000 of 6748 papers

TitleStatusHype
Modeling Polyp Activity of Paragorgia arborea Using Supervised Learning—0
Liquid Structural State-Space ModelsCode2
Myopia prediction for adolescents via time-aware deep learning—0
Neural State-Space Modeling with Latent Causal-Effect DisentanglementCode0
A Deep Learning Approach to Analyzing Continuous-Time SystemsCode1
High-Resolution Satellite Imagery for Modeling the Impact of Aridification on Crop Production—0
Asset Pricing and Deep Learning—0
DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions—0
Physics-Informed Graph Neural Network for Spatial-temporal Production Forecasting—0
Multivariate Wasserstein Functional Connectivity for Autism Screening—0
Time Series Causal Link Estimation under Hidden Confounding using Knockoff Interventions—0
A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics—0
Deep learning based sferics recognition for AMT data processing in the dead band—0
Optimal Stopping with Gaussian Processes—0
STING: Self-attention based Time-series Imputation Networks using GAN—0
StyleTime: Style Transfer for Synthetic Time Series Generation—0
Robust Forecasting for Robotic Control: A Game-Theoretic Approach—0
Multiscale Comparison of Nonparametric Trend Curves—0
Combined Unbalanced Distribution System State and Line Impedance Matrix Estimation—0
OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye SemanticsCode1
Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural NetworksCode0
An Image Processing approach to identify solar plages observed at 393.37 nm by the Kodaikanal Solar ObservatoryCode0
Contrastive Learning for Time Series on Dynamic Graphs—0
Review of Time Series Forecasting Methods and Their Applications to Particle Accelerators—0
DeepVARwT: Deep Learning for a VAR Model with TrendCode1
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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