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 1701–1750 of 6748 papers

TitleStatusHype
Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models—0
DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data—0
Detecting early signs of depressive and manic episodes in patients with bipolar disorder using the signature-based model—0
Deep Anomaly Detection for Time-series Data in Industrial IoT: A Communication-Efficient On-device Federated Learning Approach—0
Concentration inequalities for correlated network-valued processes with applications to community estimation and changepoint analysis—0
Concealer: SGX-based Secure, Volume Hiding, and Verifiable Processing of Spatial Time-Series Datasets—0
Computer Model Calibration with Time Series Data using Deep Learning and Quantile Regression—0
Deep Baseline Network for Time Series Modeling and Anomaly Detection—0
Arm order recognition in multi-armed bandit problem with laser chaos time series—0
Deep Bayesian Nonparametric Tracking—0
A minor extension of the logistic equation for growth of word counts on online media: Parametric description of diversity of growth phenomena in society—0
Computer activity learning from system call time series—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
Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases—0
ARMDN: Associative and Recurrent Mixture Density Networks for eRetail Demand Forecasting—0
Compressive Nonparametric Graphical Model Selection For Time Series—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
Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification—0
Comprehensive Time-Series Regression Models Using GRETL -- U.S. GDP and Government Consumption Expenditures & Gross Investment from 1980 to 2013—0
A metric to compare the anatomy variation between image time series—0
Augmenting Physiological Time Series Data: A Case Study for Sleep Apnea Detection—0
Deep Decomposition for Stochastic Normal-Abnormal Transport—0
A Data-Driven Approach for Predicting Vegetation-Related Outages in Power Distribution Systems—0
Augmenting transferred representations for stock classification—0
Deep Directed Information-Based Learning for Privacy-Preserving Smart Meter Data Release—0
A Unified Framework for Long Range and Cold Start Forecasting of Seasonal Profiles in Time Series—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
Comprehensive Review of Neural Differential Equations for Time Series Analysis—0
ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without Periodogram and Gaussianity Assumptions—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
Accurate shape and phase averaging of time series through Dynamic Time Warping—0
Composition Properties of Inferential Privacy for Time-Series Data—0
Evaluating the Planning and Operational Resilience of Electrical Distribution Systems with Distributed Energy Resources using Complex Network Theory—0
Deep Factors for Forecasting—0
Deep Transformer Model with Pre-Layer Normalization for COVID-19 Growth Prediction—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
AutoCTS: Automated Correlated Time Series Forecasting -- Extended Version—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
A review on outlier/anomaly detection in time series data—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