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 3401–3425 of 6748 papers

TitleStatusHype
Feature space approximation for kernel-based supervised learningCode0
Deep Sequence Learning for Accurate Gestational Age Estimation from a \$25 Doppler Device—0
Asymmetric excitation of left- and right-tail extreme events probed using a Hawkes model: application to financial returns—0
RTFN: A Robust Temporal Feature Network for Time Series Classification—0
A Non-linear Function-on-Function Model for Regression with Time Series Data—0
Improving Clinical Outcome Predictions Using Convolution over Medical Entities with Multimodal LearningCode1
Gaussian Processes for Traffic Speed Prediction at Different Aggregation Levels—0
CASU2Net: Cascaded Unification Network by a Two-step Early Fusion for Fault Detection in Offshore Wind Turbines—0
Discovering Hidden Physics Behind Transport Dynamics—0
Marginalised Spectral Mixture Kernels with Nested Sampling—0
Scalable Hybrid Hidden Markov Model with Gaussian Process Emission for Sequential Time-series Observations—0
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
Analysis of Empirical Mode Decomposition-based Load and Renewable Time Series Forecasting—0
Time Series Data Imputation: A Survey on Deep Learning Approaches—0
Predictive maintenance on event logs: Application on an ATM fleet—0
A Worrying Analysis of Probabilistic Time-series Models for Sales Forecasting—0
Central and Non-central Limit Theorems arising from the Scattering Transform and its Neural Activation Generalization—0
Continuous Ant-Based Neural Topology Search—0
Quickest Detection of COVID-19 Pandemic Onset—0
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification—0
Deep Directed Information-Based Learning for Privacy-Preserving Smart Meter Data Release—0
Two-Step Meta-Learning for Time-Series Forecasting Ensemble—0
A Two-Way Transformed Factor Model for Matrix-Variate Time Series—0
Distributional uncertainty of the financial time series measured by G-expectation—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