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 2551–2575 of 6748 papers

TitleStatusHype
Ensemble of Hankel Matrices for Face Emotion Recognition—0
Extension of causal decomposition in the mutual complex dynamic process—0
EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid—0
Extract Dynamic Information To Improve Time Series Modeling: a Case Study with Scientific Workflow—0
Flow Forecast: A deep learning for time series forecasting, classification, and anomaly detection framework built in PyTorch—0
Extracting Predictive Information from Heterogeneous Data Streams using Gaussian Processes—0
Extracting Traffic Primitives Directly from Naturalistically Logged Data for Self-Driving Applications—0
Stochastic volatility modeling of high-frequency CSI 300 index and dynamic jump prediction driven by machine learning—0
Focusing on What is Relevant: Time-Series Learning and Understanding using Attention—0
Ensemble neuroevolution based approach for multivariate time series anomaly detection—0
Extreme-Long-short Term Memory for Time-series Prediction—0
Extreme-SAX: Extreme Points Based Symbolic Representation for Time Series Classification—0
Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphs—0
Eye Know You Too: A DenseNet Architecture for End-to-end Eye Movement Biometrics—0
Facial Expression Classification Using Rotation Slepian-based Moment Invariants—0
Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series—0
Ensemble manifold based regularized multi-modal graph convolutional network for cognitive ability prediction—0
Factorized Inference in Deep Markov Models for Incomplete Multimodal Time Series—0
Factor Network Autoregressions—0
Fading of collective attention shapes the evolution of linguistic variants—0
Failure Analysis on Multivariate Time-series Data given Uncertain Labels—0
Application of Machine Learning to accidents detection at directional drilling—0
Fairness in Forecasting and Learning Linear Dynamical Systems—0
Applying Nature-Inspired Optimization Algorithms for Selecting Important Timestamps to Reduce Time Series Dimensionality—0
Causal Structural Learning from Time Series: A Convex Optimization Approach—0
Show:102550
← PrevPage 103 of 270Next →
All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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