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

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Showing 2001–2050 of 6748 papers

TitleStatusHype
An Introductory Study on Time Series Modeling and Forecasting—0
Digital Twin Framework for Time to Failure Forecasting of Wind Turbine Gearbox: A Concept—0
Digital biomarkers and artificial intelligence for mass diagnosis of atrial fibrillation in a population sample at risk of sleep disordered breathing—0
Diffusion Maps meet Nyström—0
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
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization—0
A Formally Robust Time Series Distance Metric—0
Differential Bayesian Neural Nets—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 Neural Architecture Search with Morphism-based Transformable Backbone Architectures—0
Discovering Causal Relations in Textual Instructions—0
Bayesian Time Series Forecasting with Change Point and Anomaly Detection—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
Differentiable Multiple Shooting Layers—0
Differentiable Dynamic Programming for Structured Prediction and Attention—0
An interpretable LSTM neural network for autoregressive exogenous model—0
Differentiable Algorithm for Marginalising Changepoints—0
Learn to Predict Vertical Track Irregularity with Extremely Imbalanced Data—0
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling—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
GeoStat Representations of Time Series for Fast Classification—0
Difference Attention Based Error Correction LSTM Model for Time Series Prediction—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
Diffeomorphic Transformations for Time Series Analysis: An Efficient Approach to Nonlinear Warping—0
Bayesian Regression Approach for Building and Stacking Predictive Models in Time Series Analytics—0
Discrete MDL Predicts in Total Variation—0
Did ChatGPT or Copilot use alter the style of internet news headlines? A time series regression analysis—0
Discrete Simulation Optimization for Tuning Machine Learning Method Hyperparameters—0
Bayesian Recurrent Framework for Missing Data Imputation and Prediction with Clinical Time Series—0
Diagnosis of systemic risk and contagion across financial sectors—0
DeVLearn: A Deep Visual Learning Framework for Localizing Temporary Faults in Power Systems—0
Bayesian Realized-GARCH Models for Financial Tail Risk Forecasting Incorporating Two-sided Weibull Distribution—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