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 1476–1500 of 6748 papers

TitleStatusHype
Context-Aware Ensemble Learning for Time Series—0
Predicting China's CPI by Scanner Big Data—0
A minor extension of the logistic equation for growth of word counts on online media: Parametric description of diversity of growth phenomena in society—0
Correlation recurrent units: A novel neural architecture for improving the predictive performance of time-series data—0
Deep Learning-Based Vehicle Speed Prediction for Ecological Adaptive Cruise Control in Urban and Highway Scenarios—0
Score-based calibration testing for multivariate forecast distributionsCode0
Sample Complexity for Evaluating the Robust Linear Observers Performance under Coprime Factors Uncertainty—0
Joint Neural Architecture and Hyperparameter Search for Correlated Time Series Forecasting—0
Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation—0
Triadic Temporal Exponential Random Graph Models (TTERGM)—0
G-CMP: Graph-enhanced Contextual Matrix Profile for unsupervised anomaly detection in sensor-based remote health monitoring—0
Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting—0
PCT-CycleGAN: Paired Complementary Temporal Cycle-Consistent Adversarial Networks for Radar-Based Precipitation Nowcasting—0
Beyond S-curves: Recurrent Neural Networks for Technology Forecasting—0
An Anomaly Detection Method for Satellites Using Monte Carlo Dropout—0
Evaluation of Entropy and Fractal Dimension as Biomarkers for Tumor Growth and Treatment Response using Cellular Automata—0
EasyMLServe: Easy Deployment of REST Machine Learning ServicesCode0
Distribution estimation and change-point estimation for time series via DNN-based GANs—0
Confidence Interval Construction for Multivariate time series using Long Short Term Memory Network—0
Machine Learning Algorithms for Time Series Analysis and Forecasting—0
EDGAR: Embedded Detection of Gunshots by AI in Real-time—0
Probabilistic Time Series Forecasting for Adaptive Monitoring in Edge Computing Environments—0
Leverage, Endogenous Unbalanced Growth, and Asset Price Bubbles—0
MGADN: A Multi-task Graph Anomaly Detection Network for Multivariate Time Series—0
Time Series Forecasting with Hypernetworks Generating Parameters in Advance—0
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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