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 3026–3050 of 6748 papers

TitleStatusHype
HeNet: A Deep Learning Approach on Intel^ Processor Trace for Effective Exploit Detection—0
Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles—0
Heterogeneous Relational Kernel Learning—0
Causal Hidden Markov Model for Time Series Disease Forecasting—0
A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series—0
"Hey, that's not an ODE'": Faster ODE Adjoints with 12 Lines of Code—0
HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection—0
Asset volatility forecasting:The optimal decay parameter in the EWMA model—0
Latent State Inference in a Spatiotemporal Generative Model—0
AGSTN: Learning Attention-adjusted Graph Spatio-Temporal Networks for Short-term Urban Sensor Value Forecasting—0
Adaptive Bayesian Sum of Trees Model for Covariate Dependent Spectral Analysis—0
Copy the dynamics using a learning machine—0
A State Space Approach for Piecewise-Linear Recurrent Neural Networks for Reconstructing Nonlinear Dynamics from Neural Measurements—0
End-to-end NILM System Using High Frequency Data and Neural Networks—0
Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding—0
Core-Collapse Supernova Gravitational-Wave Search and Deep Learning Classification—0
Context-tree weighting for real-valued time series: Bayesian inference with hierarchical mixture models—0
Hierarchical Clustering for Smart Meter Electricity Loads based on Quantile Autocovariances—0
Hierarchical Clustering using Auto-encoded Compact Representation for Time-series Analysis—0
Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series—0
Hierarchical Estimation for Effective and Efficient Sampling Graph Neural Network—0
Hierarchical Fisher Kernels for Longitudinal Data—0
Causal Graph Discovery from Self and Mutually Exciting Time Series—0
Hierarchical Graph Neural Networks for Causal Discovery and Root Cause Localization—0
A Novel Framework for Handling Sparse Data in Traffic Forecast—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