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 826–850 of 6748 papers

TitleStatusHype
Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion—0
Boosted p-Values for High-Dimensional Vector Autoregression—0
Time series quantile regression using random forests—0
Collaborative Multiobjective Evolutionary Algorithms in search of better Pareto Fronts. An application to trading systems—0
MUSTACHE: Multi-Step-Ahead Predictions for Cache Eviction—0
On Estimation and Inference of Large Approximate Dynamic Factor Models via the Principal Component Analysis—0
Demo: LE3D: A Privacy-preserving Lightweight Data Drift Detection Framework—0
Robust Time Series Chain Discovery with Incremental Nearest Neighbors—0
Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantificationCode0
Physics-inspired machine learning for power grid frequency modelling—0
Geodesic Sinkhorn for Fast and Accurate Optimal Transport on Manifolds—0
Evaluating Impact of Social Media Posts by Executives on Stock PricesCode0
Recurrent Neural Networks and Universal Approximation of Bayesian Filters—0
Infinite-Dimensional Adaptive Boundary Observer for Inner-Domain Temperature Estimation of 3D Electrosurgical Processes using Surface Thermography Sensing—0
Learning Task-Aware Effective Brain Connectivity for fMRI Analysis with Graph Neural NetworksCode1
HFN: Heterogeneous Feature Network for Multivariate Time Series Anomaly Detection—0
A novel approach to quantify volatility prediction—0
Spatial-Temporal Synchronous Graph Transformer network (STSGT) for COVID-19 forecastingCode1
Variational Inference Aided Estimation of Time Varying Channels—0
Ensemble transport smoothing. Part I: Unified frameworkCode0
Denoising neural networks for magnetic resonance spectroscopy—0
MambaNet: A Hybrid Neural Network for Predicting the NBA Playoffs—0
Diffusion models for missing value imputation in tabular dataCode1
Probabilistic Decomposition Transformer for Time Series ForecastingCode1
Uncertainty-DTW for Time Series and SequencesCode0
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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