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 2576–2600 of 6748 papers

TitleStatusHype
Learning Non-Stationary Time-Series with Dynamic Pattern Extractions—0
Vehicular Visible Light Communications Noise Analysis and Autoencoder Based Denoising—0
Unsupervised Visual Time-Series Representation Learning and Clustering—0
DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behavior—0
A transformer-based model for default prediction in mid-cap corporate markets—0
How News Evolves? Modeling News Text and Coverage using Graphs and Hawkes Process—0
Smart Data Representations: Impact on the Accuracy of Deep Neural NetworksCode0
GAETS: A Graph Autoencoder Time Series Approach Towards Battery Parameter Estimation—0
Switching Recurrent Kalman Networks—0
Uncertainty-Aware Multiple Instance Learning from Large-Scale Long Time Series Data—0
Online Advertising Revenue Forecasting: An Interpretable Deep Learning Approach—0
Outlier Detection as Instance Selection Method for Feature Selection in Time Series Classification—0
Graph neural network-based fault diagnosis: a review—0
Machine Learning for Genomic Data—0
On Sparse High-Dimensional Graphical Model Learning For Dependent Time Series—0
A similarity measurement for time series and its application to the stock market—0
Decoding Causality by Fictitious VAR Modeling—0
Forecasting Crude Oil Price Using Event Extraction—0
Nyström Regularization for Time Series Forecasting—0
LoMEF: A Framework to Produce Local Explanations for Global Model Time Series Forecasts—0
Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes—0
Soft Sensing Model Visualization: Fine-tuning Neural Network from What Model Learned—0
Identifying On-road Scenarios Predictive of ADHD usingDriving Simulator Time Series Data—0
Learning Quantile Functions without Quantile Crossing for Distribution-free Time Series Forecasting—0
A Time-Series Scale Mixture Model of EEG with a Hidden Markov Structure for Epileptic Seizure Detection—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