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 2001–2025 of 6748 papers

TitleStatusHype
An Introduction to Animal Movement Modeling with Hidden Markov Models using Stan for Bayesian Inference—0
A Fourier Transform Approach for Automatic Detection of Oysters Spawning—0
Behavioural Analytics: Mathematics of the Mind—0
Differential Recurrent Neural Networks for Action Recognition—0
Behave-XAI: Deep Explainable Learning of Behavioral Representational Data—0
Differentially Private K-means Clustering Applied to Meter Data Analysis and Synthesis—0
Differentially-Private Heat and Electricity Markets Coordination—0
BayesLDM: A Domain-Specific Language for Probabilistic Modeling of Longitudinal Data—0
An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data—0
Digital Twin Framework for Time to Failure Forecasting of Wind Turbine Gearbox: A Concept—0
A Formally Robust Time Series Distance Metric—0
Differential Bayesian Neural Nets—0
Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures—0
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization—0
Bayesian Time Series Forecasting with Change Point and Anomaly Detection—0
Differentiable Multiple Shooting Layers—0
Dimension Reduction for time series with Variational AutoEncoders—0
Direct detection of pixel-level myocardial infarction areas via a deep-learning algorithm—0
Direct Estimation of Pharmacokinetic Parameters from DCE-MRI using Deep CNN with Forward Physical Model Loss—0
Direct Load Control of Thermostatically Controlled Loads Based on Sparse Observations Using Deep Reinforcement Learning—0
Direct Mapping Hidden Excited State Interaction Patterns from ab initio Dynamics and Its Implications on Force Field Development—0
Direct Method for Training Feed-forward Neural Networks using Batch Extended Kalman Filter for Multi-Step-Ahead Predictions—0
Differentiable Dynamic Programming for Structured Prediction and Attention—0
Discovering Causal Relations in Textual Instructions—0
An interpretable LSTM neural network for autoregressive exogenous model—0
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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