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 2851–2875 of 6748 papers

TitleStatusHype
Wavelet analysis and energy-based measures for oil-food price relationship as a footprint of financialisation effect—0
Learning Hamiltonian dynamics by reservoir computer—0
Neural circuits for dynamics-based segmentation of time seriesCode0
Deep Convolutional Neural Network for Non-rigid Image Registration—0
Predicting the Number of Reported Bugs in a Software RepositoryCode0
A study on Ensemble Learning for Time Series Forecasting and the need for Meta-Learning—0
Normalized multivariate time series causality analysis and causal graph reconstruction—0
Sequential convolutional network for behavioral pattern extraction in gait recognition—0
Time Series Forecasting via Learning Convolutionally Low-Rank ModelsCode0
A Feature Selection Method for Multi-Dimension Time-Series Data—0
Real-time NLOS/LOS Identification for Smartphone-based Indoor Positioning System using WiFi RTT and RSS—0
Time series analysis with dynamic law exploration—0
Scalable Predictive Time-Series Analysis of COVID-19: Cases and Fatalities—0
Enhancing predictive skills in physically-consistent way: Physics Informed Machine Learning for Hydrological Processes—0
Survey on Modeling Intensity Function of Hawkes Process Using Neural Models—0
3KG: Contrastive Learning of 12-Lead Electrocardiograms using Physiologically-Inspired Augmentations—0
Winterization of Texan power system infrastructure is profitable but risky—0
Aedes-AI: Neural Network Models of Mosquito Abundance—0
A windowed correlation based feature selection method to improve time series prediction of dengue fever cases—0
Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series—0
A Comparative Study of Using Spatial-Temporal Graph Convolutional Networks for Predicting Availability in Bike Sharing Schemes—0
Principal Component Density Estimation for Scenario Generation Using Normalizing Flows—0
Learning future terrorist targets through temporal meta-graphsCode1
Using CNNs for AD classification based on spatial correlation of BOLD signals during the observation—0
A geometric approach to conditioning belief functions—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