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

Showing 62516300 of 6748 papers

TitleStatusHype
Low Dimensional Convolutional Neural Network For Solar Flares GOES Time Series ClassificationCode0
ConFuse: Convolutional Transform Learning Fusion Framework For Multi-Channel Data AnalysisCode0
Adversarial Constraint Learning for Structured PredictionCode0
Conformal Prediction Intervals with Temporal DependenceCode0
Flow-based Spatio-Temporal Structured Prediction of Motion DynamicsCode0
Scaling up Dynamic Topic ModelsCode0
A Review of Network Inference Techniques for Neural Activation Time SeriesCode0
Performance evaluation of deep neural networks for forecasting time-series with multiple structural breaks and high volatilityCode0
Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather ForecastingCode0
Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural NetworksCode0
Unsupervised Representation Learning of Structured Radio Communication SignalsCode0
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time SeriesCode0
Flipped Classroom: Effective Teaching for Time Series ForecastingCode0
FLICU: A Federated Learning Workflow for Intensive Care Unit Mortality PredictionCode0
Score-based calibration testing for multivariate forecast distributionsCode0
Achieving Risk Control in Online Learning SettingsCode0
Coordination Event Detection and Initiator Identification in Time Series DataCode0
LSTM-MSNet: Leveraging Forecasts on Sets of Related Time Series with Multiple Seasonal PatternsCode0
Condition Monitoring of Drive Trains by Data Fusion of Acoustic Emission and Vibration SensorsCode0
Conditioning non-linear and infinite-dimensional diffusion processesCode0
Lyapunov-Guided Representation of Recurrent Neural Network PerformanceCode0
M3Fusion: A Deep Learning Architecture for Multi-Scale/Modal/Temporal satellite data fusionCode0
Subspace Identification of Temperature DynamicsCode0
Time Series Alignment with Global InvariancesCode0
Towards dense object tracking in a 2D honeybee hiveCode0
Machine Learning Algorithms to Assess Site Closure Time Frames for Soil and Groundwater ContaminationCode0
Unsupervised Scalable Representation Learning for Multivariate Time SeriesCode0
Auxiliary Quantile Forecasting with Linear NetworksCode0
An attention model to analyse the risk of agitation and urinary tract infections in people with dementiaCode0
A Recurrent Neural Network Survival Model: Predicting Web User Return TimeCode0
Autoregressive Convolutional Recurrent Neural Network for Univariate and Multivariate Time Series PredictionCode0
Deep Particulate Matter Forecasting Model Using Correntropy-Induced LossCode0
Phase Harmonic Correlations and Convolutional Neural NetworksCode0
Anamnesic Neural Differential Equations with Orthogonal Polynomial ProjectionsCode0
Condition Assessment of Stay Cables through Enhanced Time Series Classification Using a Deep Learning ApproachCode0
Fitting stochastic predator-prey models using both population density and kill rate dataCode0
Adaptive pooling operators for weakly labeled sound event detectionCode0
Uncertainty-Aware Variational-Recurrent Imputation Network for Clinical Time SeriesCode0
Time Series Analysis of Blockchain-Based Cryptocurrency Price ChangesCode0
Machine Learning for Neuroimaging with Scikit-LearnCode0
Analyzing Linear Dynamical Systems: From Modeling to Coding and LearningCode0
Uncertainty-DTW for Time Series and SequencesCode0
Conditional Time Series Forecasting with Convolutional Neural NetworksCode0
Supporting Optimal Phase Space Reconstructions Using Neural Network Architecture for Time Series ModelingCode0
Towards Neural Numeric-To-Text Generation From Temporal Personal Health DataCode0
Physics-Driven Learning of Wasserstein GAN for Density Reconstruction in Dynamic TomographyCode0
Support Vector Machines with Time Series Distance Kernels for Action ClassificationCode0
Seglearn: A Python Package for Learning Sequences and Time SeriesCode0
Accelerating Neural Architecture Search using Performance PredictionCode0
Machine learning modeling for time series problem: Predicting flight ticket pricesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1naive classifierF187.47Unverified
2GRU-D - APC (n = 1)F127.3Unverified
3GRU-APC (n = 1)F125.7Unverified
4GRU-DF122.5Unverified
5GRUF122.3Unverified
6GRU-SimpleF122.2Unverified
7GRU-MeanF122.1Unverified
#ModelMetricClaimedVerifiedStatus
1SepTr% Test Accuracy98.51Unverified
2ViT% Test Accuracy98.11Unverified
3FlexTCN-4% Test Accuracy97.73Unverified
4MatchboxNet% Test Accuracy97.4Unverified
5CKCNN (100k)% Test Accuracy95.27Unverified
6FlexTCN-6% Test Accuracy (Raw Data)91.73Unverified
#ModelMetricClaimedVerifiedStatus
1ResBiLSTMMAE0.13Unverified