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 3451–3475 of 6748 papers

TitleStatusHype
Low Dimensional Convolutional Neural Network For Solar Flares GOES Time Series ClassificationCode0
Mining the Mind: Linear Discriminant Analysis of MEG source reconstruction time series supports dynamic changes in deep brain regions during meditation sessions—0
Gesture Recognition in Robotic Surgery: a Review—0
Deep Generative SToRM model for dynamic imaging—0
Inference of stochastic time series with missing data—0
Embedding Symbolic Temporal Knowledge into Deep Sequential Models—0
Echo State Network for two-dimensional turbulent moist Rayleigh-Bénard convection—0
Indian Economy and Nighttime Lights—0
Statistical guided-waves-based SHM via stochastic non-parametric time series models—0
Short-term prediction of Time Series based on bounding techniques—0
A fast algorithm for complex discord searches in time series: HOT SAX Time—0
Identification of brain states, transitions, and communities using functional MRI—0
Spectrum Attention Mechanism for Time Series Classification—0
Temporal Latent Auto-Encoder: A Method for Probabilistic Multivariate Time Series Forecasting—0
Optimizing Convergence for Iterative Learning of ARIMA for Stationary Time Series—0
Multi-view Integration Learning for Irregularly-sampled Clinical Time SeriesCode0
VConstruct: Filling Gaps in Chl-a Data Using a Variational Autoencoder—0
Conditional Generative Models for Counterfactual Explanations—0
Dynamic cyber risk estimation with Competitive Quantile AutoregressionCode0
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
Analysis of stock index with a generalized BN-S model: an approach based on machine learning and fuzzy parameters—0
A Review on Deep Learning in UAV Remote Sensing—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