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 751–775 of 6748 papers

TitleStatusHype
EasyMLServe: Easy Deployment of REST Machine Learning ServicesCode0
Distribution estimation and change-point estimation for time series via DNN-based GANs—0
EDGAR: Embedded Detection of Gunshots by AI in Real-time—0
Machine Learning Algorithms for Time Series Analysis and Forecasting—0
Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network—0
Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments—0
Leverage, Endogenous Unbalanced Growth, and Asset Price Bubbles—0
Neural Superstatistics for Bayesian Estimation of Dynamic Cognitive ModelsCode1
Time Series Forecasting with Hypernetworks Generating Parameters in Advance—0
MGADN: A Multi-task Graph Anomaly Detection Network for Multivariate Time Series—0
Understanding the Vulnerability of Skeleton-based Human Activity Recognition via Black-box AttackCode1
Deep learning delay coordinate dynamics for chaotic attractors from partial observable data—0
Machine Learning Methods for Anomaly Detection in Nuclear Power Plant Power Transformers—0
Finding active galactic nuclei through FinkCode1
Estimating Task Completion Times for Network Rollouts using Statistical Models within Partitioning-based Regression Methods—0
Class-Specific Attention (CSA) for Time-Series Classification—0
Autoregressive GNN-ODE GRU Model for Network Dynamics—0
Step Counting with Attention-based LSTMCode0
Identifying Unique Causal Network from Nonstationary Time SeriesCode0
Fractional integration and cointegration—0
A Transformer Framework for Data Fusion and Multi-Task Learning in Smart CitiesCode0
Imputation of Missing Streamflow Data at Multiple Gauging Stations in Benin Republic—0
DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift—0
Cointegration with Occasionally Binding Constraints—0
Neural Inference of Gaussian Processes for Time Series Data of QuasarsCode0
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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