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 2351–2400 of 6748 papers

TitleStatusHype
Automated Detection of Left Ventricle in Arterial Input Function Images for Inline Perfusion Mapping using Deep Learning: A study of 15,000 Patients—0
An empirical study of neural networks for trend detection in time series—0
Interpretable Deep Representation Learning from Temporal Multi-view Data—0
An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks—0
Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking—0
Deep Hedging under Rough Volatility—0
Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection—0
Deep Hedging, Generative Adversarial Networks, and Beyond—0
Deep Haar Scattering Networks in Pattern Recognition: A promising approach—0
DeepGuard: A Framework for Safeguarding Autonomous Driving Systems from Inconsistent Behavior—0
Autoencoding Time Series for Visualisation—0
An Empirical Study on How the Developers Discussed about Pandas Topics—0
Adversarial attacks against Bayesian forecasting dynamic models—0
A Comparison of Model-Free and Model Predictive Control for Price Responsive Water Heaters—0
Deep Generators on Commodity Markets; application to Deep Hedging—0
Autoencoding Conditional GAN for Portfolio Allocation Diversification—0
Deep Generative SToRM model for dynamic imaging—0
Deep Generative Quantile-Copula Models for Probabilistic Forecasting—0
Autoencoder-based time series clustering with energy applications—0
Deep Gaussian Covariance Network—0
An Empirical Exploration of Deep Recurrent Connections and Memory Cells Using Neuro-Evolution—0
Deep-Gap: A deep learning framework for forecasting crowdsourcing supply-demand gap based on imaging time series and residual learning—0
Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems—0
Deep Fusion of Lead-lag Graphs: Application to Cryptocurrencies—0
AutoCTS: Automated Correlated Time Series Forecasting -- Extended Version—0
An Empirical Evaluation of Similarity Measures for Time Series Classification—0
DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data—0
DeepFIB: Self-Imputation for Time Series Anomaly Detection—0
AutoAI-TS: AutoAI for Time Series Forecasting—0
Deep Federated Anomaly Detection for Multivariate Time Series Data—0
Deep Factors with Gaussian Processes for Forecasting—0
Autism Spectrum Disorder Classification using Graph Kernels on Multidimensional Time Series—0
Advancing multivariate time series similarity assessment: an integrated computational approach—0
A Comparison of LSTMs and Attention Mechanisms for Forecasting Financial Time Series—0
Deep Factors for Forecasting—0
Deep-ESN: A Multiple Projection-encoding Hierarchical Reservoir Computing Framework—0
A user-centric model of voting intention from Social Media—0
Deep Ensemble Tensor Factorization for Longitudinal Patient Trajectories Classification—0
Deep EHR Spotlight: a Framework and Mechanism to Highlight Events in Electronic Health Records for Explainable Predictions—0
A Unified SVM Framework for Signal Estimation—0
Deep Echo State Networks for Diagnosis of Parkinson's Disease—0
A Unified Method for First and Third Person Action Recognition—0
Advancing Enterprise Spatio-Temporal Forecasting Applications: Data Mining Meets Instruction Tuning of Language Models For Multi-modal Time Series Analysis in Low-Resource Settings—0
Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series—0
Deep-dust: Predicting concentrations of fine dust in Seoul using LSTM—0
A unified machine learning approach to time series forecasting applied to demand at emergency departments—0
DeepDPM: Dynamic Population Mapping via Deep Neural Network—0
Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices—0
A unified framework of epidemic spreading prediction by empirical mode decomposition based ensemble learning techniques—0
Deep Directed Information-Based Learning for Privacy-Preserving Smart Meter Data Release—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