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 2951–2975 of 6748 papers

TitleStatusHype
Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series—0
Conditional heteroskedasticity in crypto-asset returns—0
Fully convolutional networks for structural health monitoring through multivariate time series classification—0
Conditional Generative Models for Counterfactual Explanations—0
Granger Mediation Analysis of Multiple Time Series with an Application to fMRI—0
Graph2Seq: Scalable Learning Dynamics for Graphs—0
A Robust Data-driven Process Modeling Applied to Time-series Stochastic Power Flow—0
Graph Attention Recurrent Neural Networks for Correlated Time Series Forecasting -- Full version—0
Frugal day-ahead forecasting of multiple local electricity loads by aggregating adaptive models—0
From time-series transcriptomics to gene regulatory networks: a review on inference methods—0
Conditional Generative Adversarial Networks to Model Urban Outdoor Air Pollution—0
A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics—0
From Time Series to Euclidean Spaces: On Spatial Transformations for Temporal Clustering—0
Graph-based Predictable Feature Analysis—0
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks—0
Graph Anomaly Detection in Time Series: A Survey—0
From Text to Trends: A Unique Garden Analytics Perspective on the Future of Modern Agriculture—0
From Static to Dynamic Node Embeddings—0
From sleep medicine to medicine during sleep: A clinical perspective—0
A Robust and Efficient Multi-Scale Seasonal-Trend Decomposition—0
From Rules to Regs: A Structural Topic Model of Collusion Research—0
Graph Hierarchical Convolutional Recurrent Neural Network (GHCRNN) for Vehicle Condition Prediction—0
Graphical estimation of multivariate count time series—0
Graphical LASSO Based Model Selection for Time Series—0
From learning gait signatures of many individuals to reconstructing gait dynamics of one single individual—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