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 801–825 of 6748 papers

TitleStatusHype
A Graph Neural Networks based Framework for Topology-Aware Proactive SLA Management in a Latency Critical NFV Application Use-case—0
Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification—0
Variance of entropy for testing time-varying regimes with an application to meme stocks—0
Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Deep Learning-Based Time Series Forecasting—0
Deep Learning for Time Series Anomaly Detection: A SurveyCode1
Active queue management: First steps toward a new control-theoretic viewpoint—0
Towards Global Crop Maps with Transfer Learning—0
Bayesian Neural Networks for Macroeconomic Analysis—0
Dynamic Interpretable Change Point Detection—0
Reduced Order Probabilistic Emulation for Physics-Based Thermosphere ModelsCode0
Motif-guided Time Series Counterfactual Explanations—0
Quantum Persistent Homology for Time Series—0
TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering—0
Privacy Meets Explainability: A Comprehensive Impact Benchmark—0
FIXED: Frustratingly Easy Domain Generalization with Mixup—0
Latent Neural ODE for Integrating Multi-timescale measurements in Smart Distribution Grids—0
Minimax Concave Penalty Regularized Adaptive System Identification—0
Automatic Change-Point Detection in Time Series via Deep LearningCode1
A Synthetic Dataset for 5G UAV Attacks Based on Observable Network Parameters—0
A Comprehensive Survey of Regression Based Loss Functions for Time Series ForecastingCode1
Discovering ordinary differential equations that govern time-series—0
Accurate and Reliable Methods for 5G UAV Jamming Identification With Calibrated Uncertainty—0
Deep learning for structural health monitoring: An application to heritage structures—0
Topological biomarkers for real-time detection of epileptic seizures—0
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion—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