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 5051–5075 of 6748 papers

TitleStatusHype
Network Intrusion Detection based on LSTM and Feature Embedding—0
NeuPDE: Neural Network Based Ordinary and Partial Differential Equations for Modeling Time-Dependent Data—0
Neural Additive Models for Nowcasting—0
Neural Architecture Searching for Facial Attributes-based Depression Recognition—0
Neural Causal Discovery with Learnable Input Noise—0
Neural CDEs for Long Time Series via the Log-ODE Method—0
Neural Coarse-Graining: Extracting slowly-varying latent degrees of freedom with neural networks—0
Neural Continuous-Time Markov Models—0
Neural Decomposition of Time-Series Data for Effective Generalization—0
Neural Dynamic Mode Decomposition for End-to-End Modeling of Nonlinear Dynamics—0
Neural Echo State Network using oscillations of gas bubbles in water—0
Neural forecasting at scale—0
Neural Generalised AutoRegressive Conditional Heteroskedasticity—0
Neural Learning of Online Consumer Credit Risk—0
Neural Machine Translation for Fact-checking Temporal Claims—0
Neural Markov Controlled SDE: Stochastic Optimization for Continuous-Time Data—0
Neural Multi-Quantile Forecasting for Optimal Inventory Management—0
Neural Network-based Automatic Factor Construction—0
Neural Network for Weighted Signal Temporal Logic—0
Neural ODEs for Informative Missingness in Multivariate Time Series—0
Neural ODE to model and prognose thermoacoustic instability—0
Neural Ordinary Differential Equation based Recurrent Neural Network Model—0
Neural Ordinary Differential Equation Model for Evolutionary Subspace Clustering and Its Applications—0
Neural Spline Search for Quantile Probabilistic Modeling—0
Neural Time Warping For Multiple Sequence Alignment—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