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 6201–6250 of 6748 papers

TitleStatusHype
Detection of Obstructive Sleep Apnoea Using Features Extracted from Segmented Time-Series ECG Signals Using a One Dimensional Convolutional Neural Network—0
Detection of small changes in medical and random-dot images comparing self-organizing map performance to human detection—0
Deterioration Prediction using Time-Series of Three Vital Signs and Current Clinical Features Amongst COVID-19 Patients—0
Determinantal Point Processes Implicitly Regularize Semi-parametric Regression Problems—0
A Deterministic Approximation to Neural SDEs—0
Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces—0
Development and Evaluation of Recurrent Neural Network based Models for Hourly Traffic Volume and AADT Prediction—0
Development of an Algorithm for Identifying Changes in System Dynamics from Time Series—0
Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents—0
Development of Deep Transformer-Based Models for Long-Term Prediction of Transient Production of Oil Wells—0
Devil in the Detail: Attack Scenarios in Industrial Applications—0
DeVLearn: A Deep Visual Learning Framework for Localizing Temporary Faults in Power Systems—0
Diagnosis of systemic risk and contagion across financial sectors—0
Did ChatGPT or Copilot use alter the style of internet news headlines? A time series regression analysis—0
Diffeomorphic Transformations for Time Series Analysis: An Efficient Approach to Nonlinear Warping—0
Difference Attention Based Error Correction LSTM Model for Time Series Prediction—0
Learn to Predict Vertical Track Irregularity with Extremely Imbalanced Data—0
Differentiable Algorithm for Marginalising Changepoints—0
Differentiable Dynamic Programming for Structured Prediction and Attention—0
Differentiable Multiple Shooting Layers—0
Differentiable Neural Architecture Search with Morphism-based Transformable Backbone Architectures—0
Differential Bayesian Neural Nets—0
Differentially-Private Heat and Electricity Markets Coordination—0
Differentially Private K-means Clustering Applied to Meter Data Analysis and Synthesis—0
Differential Recurrent Neural Networks for Action Recognition—0
Diffusion Maps meet Nyström—0
Digital biomarkers and artificial intelligence for mass diagnosis of atrial fibrillation in a population sample at risk of sleep disordered breathing—0
Digital Twin Framework for Time to Failure Forecasting of Wind Turbine Gearbox: A Concept—0
Dimensionality Expansion of Load Monitoring Time Series and Transfer Learning for EMS—0
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization—0
Dimensionality reduction for time series data—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
Direct Signal Separation Via Extraction of Local Frequencies with Adaptive Time-Varying Parameters—0
Discovering Causal Relations in Textual Instructions—0
Discovering Common Change-Point Patterns in Functional Connectivity Across Subjects—0
Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets—0
Discovering Hidden Physics Behind Transport Dynamics—0
Discovering Invariances in Healthcare Neural Networks—0
Discovering ordinary differential equations that govern time-series—0
Discovering Playing Patterns: Time Series Clustering of Free-To-Play Game Data—0
Discovering Potential Correlations via Hypercontractivity—0
Discovering Latent Covariance Structures for Multiple Time Series—0
Discovering Signals from Web Sources to Predict Cyber Attacks—0
Discovering Volatile Events in Your Neighborhood: Local-Area Topic Extraction from Blog Entries—0
Discovery of Important Subsequences in Electrocardiogram Beats Using the Nearest Neighbour Algorithm—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