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 676–700 of 6748 papers

TitleStatusHype
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet SpaceCode1
Improving Clinical Outcome Predictions Using Convolution over Medical Entities with Multimodal LearningCode1
Remaining Useful Life Estimation Under Uncertainty with Causal GraphNetsCode1
Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time IntervalsCode1
Solving high-dimensional parameter inference: marginal posterior densities & Moment NetworksCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
Tabular Transformers for Modeling Multivariate Time SeriesCode1
Classification of Periodic Variable Stars with Novel Cyclic-Permutation Invariant Neural NetworksCode1
Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports DatasetCode1
Benchmarking Deep Learning Interpretability in Time Series PredictionsCode1
Inter-Series Attention Model for COVID-19 ForecastingCode1
A biologically plausible neural network for Slow Feature AnalysisCode1
A Review of Deep Learning Methods for Irregularly Sampled Medical Time Series DataCode1
Predicting human decision making in psychological tasks with recurrent neural networksCode1
Probabilistic Numeric Convolutional Neural NetworksCode1
Neural Additive Vector Autoregression Models for Causal Discovery in Time SeriesCode1
Neural Ordinary Differential Equations for Intervention ModelingCode1
An Accurate and Fully-Automated Ensemble Model for Weekly Time Series ForecastingCode1
Differentiable Divergences Between Time SeriesCode1
Decomposing non-stationary signals with time-varying wave-shape functionsCode1
Probabilistic Time Series Forecasting with Structured Shape and Temporal DiversityCode1
VEST: Automatic Feature Engineering for ForecastingCode1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
A Transformer-based Framework for Multivariate Time Series Representation LearningCode1
Online Neural Networks for Change-Point DetectionCode1
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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