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 1951–2000 of 6748 papers

TitleStatusHype
Comparison of Uncertainty Quantification with Deep Learning in Time Series Regression—0
Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Cases—0
A Review of Open Source Software Tools for Time Series Analysis—0
Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving—0
Detecting Gas Vapor Leaks Using Uncalibrated Sensors—0
Detecting Handwritten Mathematical Terms with Sensor Based Data—0
Detecting Hardly Visible Roads in Low-Resolution Satellite Time Series Data—0
Detecting Nonlinear Causality in Multivariate Time Series with Sparse Additive Models—0
Comparison of Recurrent Neural Network Architectures for Wildfire Spread Modelling—0
Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization—0
Detecting residues of cosmic events using residual neural network—0
Detecting Rough Volatility: A Filtering Approach—0
Detecting Slag Formations with Deep Convolutional Neural Networks—0
Detecting Structural Breaks in Foreign Exchange Markets by using the group LASSO technique—0
Bayesian multi--dipole localization and uncertainty quantification from simultaneous EEG and MEG recordings—0
Comparison of PCA with ICA from data distribution perspective—0
A Review of Mathematical and Computational Methods in Cancer Dynamics—0
The amplitude modulation pattern of Gaussian noise is a fingerprint of Gaussianity—0
Comparison of Machine Learning Methods for Predicting Karst Spring Discharge in North China—0
Comparison of LSTM autoencoder based deep learning enabled Bayesian inference using two time series reconstruction approaches—0
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
Bayesian nonparametric sparse VAR models—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
A Context Integrated Relational Spatio-Temporal Model for Demand and Supply Forecasting—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
American Hate Crime Trends Prediction with Event Extraction—0
Dynamic Advisor-Based Ensemble (dynABE): Case study in stock trend prediction of critical metal companies—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
Bayesian Recurrent Framework for Missing Data Imputation and Prediction with Clinical Time Series—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
Dynamical prediction of two meteorological factors using the deep neural network and the long short-term memory (2)—0
Dynamic and Context-Dependent Stock Price Prediction Using Attention Modules and News Sentiment—0
Comparison of end-to-end neural network architectures and data augmentation methods for automatic infant motility assessment using wearable sensors—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
Comparison of Different Methods for Time Sequence Prediction in Autonomous Vehicles—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