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 1526–1550 of 6748 papers

TitleStatusHype
Data Quality Over Quantity: Pitfalls and Guidelines for Process Analytics—0
Comparison of Uncertainty Quantification with Deep Learning in Time Series Regression—0
Accounting for Temporal Variability in Functional Magnetic Resonance Imaging Improves Prediction of IntelligenceCode0
Spatial Temporal Graph Convolution with Graph Structure Self-learning for Early MCI Detection—0
Investigating Enhancements to Contrastive Predictive Coding for Human Activity RecognitionCode0
Does Deep Learning REALLY Outperform Non-deep Machine Learning for Clinical Prediction on Physiological Time Series?—0
Variance of entropy for testing time-varying regimes with an application to meme stocks—0
Driver Maneuver Detection and Analysis using Time Series Segmentation and Classification—0
A Graph Neural Networks based Framework for Topology-Aware Proactive SLA Management in a Latency Critical NFV Application Use-case—0
Bayesian Neural Networks for Macroeconomic Analysis—0
Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Deep Learning-Based Time Series Forecasting—0
Towards Global Crop Maps with Transfer Learning—0
Active queue management: First steps toward a new control-theoretic viewpoint—0
TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering—0
Reduced Order Probabilistic Emulation for Physics-Based Thermosphere ModelsCode0
Dynamic Interpretable Change Point Detection—0
Motif-guided Time Series Counterfactual Explanations—0
Privacy Meets Explainability: A Comprehensive Impact Benchmark—0
Quantum Persistent Homology for Time Series—0
Latent Neural ODE for Integrating Multi-timescale measurements in Smart Distribution Grids—0
Minimax Concave Penalty Regularized Adaptive System Identification—0
FIXED: Frustratingly Easy Domain Generalization with Mixup—0
A Synthetic Dataset for 5G UAV Attacks Based on Observable Network Parameters—0
Accurate and Reliable Methods for 5G UAV Jamming Identification With Calibrated Uncertainty—0
Discovering ordinary differential equations that govern time-series—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