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 5851–5900 of 6748 papers

TitleStatusHype
Context-dependent self-exciting point processes: models, methods, and risk bounds in high dimensions—0
Context Dependent Semantic Parsing over Temporally Structured Data—0
Context-invariant, multi-variate time series representations—0
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions—0
Continual Learning Using Bayesian Neural Networks—0
Continuous Ant-Based Neural Topology Search—0
Continuous Convolutional Neural Network forNonuniform Time Series—0
Continuous Convolutional Neural Networks: Coupled Neural PDE and ODE—0
Continuous Sign Language Recognition via Temporal Super-Resolution Network—0
Contour map of estimation error for Expected Shortfall—0
ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces—0
Contrastive Conditional Neural Processes—0
Learning Informative Health Indicators Through Unsupervised Contrastive Learning—0
Contrastive Learning for Time Series on Dynamic Graphs—0
Contrastive Learning Is Not Optimal for Quasiperiodic Time Series—0
Contrastive learning of strong-mixing continuous-time stochastic processes—0
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition—0
Contrastive Multivariate Singular Spectrum Analysis—0
Contrastive predictive coding for Anomaly Detection in Multi-variate Time Series Data—0
Contributions to Large Scale Bayesian Inference and Adversarial Machine Learning—0
Controlled time series generation for automotive software-in-the-loop testing using GANs—0
Controlling Contents in Data-to-Document Generation with Human-Designed Topic Labels—0
Controlling False Discovery Rates under Cross-Sectional Correlations—0
Convergence of GANs Training: A Game and Stochastic Control Methodology—0
Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations—0
Convolutional Mixture Density Recurrent Neural Network for Predicting User Location with WiFi Fingerprints—0
Convolutional Neural Network-Bagged Decision Tree: A hybrid approach to reduce electric vehicle's driver's range anxiety by estimating energy consumption in real-time—0
Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation—0
Convolutional Neural Networks: Basic Concepts and Applications in Manufacturing—0
Convolutional Neural Networks for Time-dependent Classification of Variable-length Time Series—0
Convolutional Sequence Modeling Revisited—0
Convolutional-Sparse-Coded Dynamic Mode Decomposition and Its Application to River State Estimation—0
Convolution, attention and structure embedding—0
ConvTimeNet: A Deep Hierarchical Fully Convolutional Model for Multivariate Time Series Analysis—0
ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification—0
Coordinating users of shared facilities via data-driven predictive assistants and game theory—0
Copy the dynamics using a learning machine—0
Core-Collapse Supernova Gravitational-Wave Search and Deep Learning Classification—0
Core Network Management Procedures for Self-Organized and Sustainable 5G Cellular Networks—0
Coresets for Kernel Regression—0
Coresets for k-Segmentation of Streaming Data—0
Coresets for Time Series Clustering—0
Predictive Modeling of Coronal Hole Areas Using Long Short-Term Memory Networks—0
Coronavirus (COVID-19): ARIMA based time-series analysis to forecast near future—0
Correcting motion induced fluorescence artifacts in two-channel neural imaging—0
Correlated Time Series Forecasting using Deep Neural Networks: A Summary of Results—0
Correlation Based Feature Subset Selection for Multivariate Time-Series Data—0
Correlations and Flow of Information between The New York Times and Stock Markets—0
Correlation-wise Smoothing: Lightweight Knowledge Extraction for HPC Monitoring Data—0
Cost-Effective Bad Synchrophasor Data Detection Based on Unsupervised Time Series Data Analytics—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