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 6001–6050 of 6748 papers

TitleStatusHype
Decentralized Flood Forecasting Using Deep Neural Networks—0
Deciphering Dynamical Nonlinearities in Short Time Series Using Recurrent Neural Networks—0
Decision-Aware Conditional GANs for Time Series Data—0
Decoding Causality by Fictitious VAR Modeling—0
Decoding dynamic brain patterns from evoked responses: A tutorial on multivariate pattern analysis applied to time-series neuroimaging data—0
Decoding Financial Health in Kenyas' Medical Insurance Sector: A Data-Driven Cluster Analysis—0
Decoding Multilingual Topic Dynamics and Trend Identification through ARIMA Time Series Analysis on Social Networks: A Novel Data Translation Framework Enhanced by LDA/HDP Models—0
Decoding of visual-related information from the human EEG using an end-to-end deep learning approach—0
Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms—0
Decoding Working Memory Load from EEG with LSTM Networks—0
Decomposing Temperature Time Series with Non-Negative Matrix Factorization—0
Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks—0
Decomposition of Time Series Data of Stock Markets and its Implications for Prediction: An Application for the Indian Auto Sector—0
Deconvolution of the Functional Ultrasound Response in the Mouse Visual Pathway Using Block-Term Decomposition—0
Deep Air Quality Forecasting Using Hybrid Deep Learning Framework—0
DeepAISE -- An End-to-End Development and Deployment of a Recurrent Neural Survival Model for Early Prediction of Sepsis—0
Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models—0
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach—0
Deep Auto-Set: A Deep Auto-Encoder-Set Network for Activity Recognition Using Wearables—0
Deep Baseline Network for Time Series Modeling and Anomaly Detection—0
Deep Bayesian Nonparametric Tracking—0
Deep Canonically Correlated LSTMs—0
Deep Canonical Time Warping—0
Deep Cellular Recurrent Network for Efficient Analysis of Time-Series Data with Spatial Information—0
Deep CHORES: Estimating Hallmark Measures of Physical Activity Using Deep Learning—0
Deep Chronnectome Learning via Full Bidirectional Long Short-Term Memory Networks for MCI Diagnosis—0
Deep Classification of Epileptic Signals—0
Deep Convolutional Architectures for Extrapolative Forecast in Time-dependent Flow Problems—0
Deep convolutional generative adversarial networks for traffic data imputation encoding time series as images—0
Deep Convolutional Neural Network for Non-rigid Image Registration—0
Deep COVID-19 Forecasting for Multiple States with Data Augmentation—0
Deep Decomposition for Stochastic Normal-Abnormal Transport—0
Deep Diabetologist: Learning to Prescribe Hyperglycemia Medications with Hierarchical Recurrent Neural Networks—0
Deep Directed Information-Based Learning for Privacy-Preserving Smart Meter Data Release—0
Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices—0
DeepDPM: Dynamic Population Mapping via Deep Neural Network—0
Deep-dust: Predicting concentrations of fine dust in Seoul using LSTM—0
Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series—0
Deep Echo State Networks for Diagnosis of Parkinson's Disease—0
Deep EHR Spotlight: a Framework and Mechanism to Highlight Events in Electronic Health Records for Explainable Predictions—0
Deep Ensemble Tensor Factorization for Longitudinal Patient Trajectories Classification—0
Deep-ESN: A Multiple Projection-encoding Hierarchical Reservoir Computing Framework—0
Deep Factors for Forecasting—0
Deep Factors with Gaussian Processes for Forecasting—0
Deep Federated Anomaly Detection for Multivariate Time Series Data—0
DeepFIB: Self-Imputation for Time Series Anomaly Detection—0
DeepFolio: Convolutional Neural Networks for Portfolios with Limit Order Book Data—0
Deep Fusion of Lead-lag Graphs: Application to Cryptocurrencies—0
Deep-Gap: A deep learning framework for forecasting crowdsourcing supply-demand gap based on imaging time series and residual learning—0
Deep Gaussian Covariance Network—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