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 3176–3200 of 6748 papers

TitleStatusHype
Short-term prediction of Time Series based on bounding techniques—0
Dynamic cyber risk estimation with Competitive Quantile AutoregressionCode0
Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting—0
Multi-Time Attention Networks for Irregularly Sampled Time SeriesCode1
Multi-view Integration Learning for Irregularly-sampled Clinical Time SeriesCode0
Optimizing Convergence for Iterative Learning of ARIMA for Stationary Time Series—0
Conditional Generative Models for Counterfactual Explanations—0
A Review of Graph Neural Networks and Their Applications in Power SystemsCode1
VConstruct: Filling Gaps in Chl-a Data Using a Variational Autoencoder—0
Spectrum Attention Mechanism for Time Series Classification—0
EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery ClassificationCode1
Multi-Task Time Series Forecasting With Shared Attention—0
Unraveling S&P500 stock volatility and networks -- An encoding-and-decoding approach—0
Short-term daily precipitation forecasting with seasonally-integrated autoencoderCode0
An Optimal Reduction of TV-Denoising to Adaptive Online Learning—0
A symbolic information approach to characterize response-related differences in cortical activity during a Go/No-Go task—0
Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters—0
Tensor-Train Networks for Learning Predictive Modeling of Multidimensional Data—0
A Review on Deep Learning in UAV Remote Sensing—0
Bayesian hierarchical stacking: Some models are (somewhere) usefulCode1
Graphical Models for Financial Time Series and Portfolio Selection—0
Where does the Stimulus go? Deep Generative Model for Commercial Banking Deposits—0
To VaR, or Not to VaR, That is the Question—0
Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction—0
Evidence and Behaviour of Support and Resistance Levels in Financial Time Series—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