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 501–525 of 6748 papers

TitleStatusHype
Noise-cleaning the precision matrix of fMRI time series—0
Deep Learning for Time Series Classification and Extrinsic Regression: A Current SurveyCode1
Surrogate uncertainty estimation for your time series forecasting black-box: learn when to trust—0
ProbPNN: Enhancing Deep Probabilistic Forecasting with Statistical Information—0
Window Size Selection in Unsupervised Time Series Analytics: A Review and BenchmarkCode1
Cross-Frequency Time Series Meta-Forecasting—0
Multivariate Time Series Anomaly Detection via Dynamic Graph Forecasting—0
A Survey on Deep Learning based Time Series Analysis with Frequency TransformationCode2
Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization—0
SimMTM: A Simple Pre-Training Framework for Masked Time-Series ModelingCode1
Sparse High-Dimensional Vector Autoregressive Bootstrap—0
Inference in Non-stationary High-Dimensional VARs—0
Learning PDE Solution Operator for Continuous Modeling of Time-Series—0
A comparative study of statistical and machine learning models on near-real-time daily emissions prediction—0
Deep COVID-19 Forecasting for Multiple States with Data Augmentation—0
A Light-weight CNN Model for Efficient Parkinson's Disease Diagnostics—0
FV-MgNet: Fully Connected V-cycle MgNet for Interpretable Time Series Forecasting—0
Monitoring the risk of a tailings dam collapse through spectral analysis of satellite InSAR time-series data—0
Time-warped Trials—0
Deep learning for ψ-weakly dependent processes—0
Adaptive hedging horizon and hedging performance estimation—0
Online estimation methods for irregular autoregressive models—0
Graph Anomaly Detection in Time Series: A Survey—0
A Bayesian Generative Adversarial Network (GAN) to Generate Synthetic Time-Series Data, Application in Combined Sewer Flow Prediction—0
Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver BehaviourCode0
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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