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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 501–525 of 6748 papers

TitleStatusHype
Efficient Optimization of Echo State Networks for Time Series DatasetsCode1
Deep Learning for Time Series Anomaly Detection: A SurveyCode1
Classification of Raw MEG/EEG Data with Detach-Rocket Ensemble: An Improved ROCKET Algorithm for Multivariate Time Series AnalysisCode1
Deep reconstruction of strange attractors from time seriesCode1
Deep Learning Statistical ArbitrageCode1
MTSS-GAN: Multivariate Time Series Simulation Generative Adversarial NetworksCode1
Explaining Time Series Predictions with Dynamic MasksCode1
Integrating Multimodal Data for Joint Generative Modeling of Complex DynamicsCode1
Deep Recurrent Model for Individualized Prediction of Alzheimer's Disease ProgressionCode1
DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series DataCode1
A Spatio-Temporal Spot-Forecasting Framework for Urban Traffic PredictionCode1
Enhancing the Robustness via Adversarial Learning and Joint Spatial-Temporal Embeddings in Traffic ForecastingCode1
Do We Really Need Deep Learning Models for Time Series Forecasting?Code1
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scalesCode1
InceptionTime: Finding AlexNet for Time Series ClassificationCode1
Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI ModellingCode1
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vesselsCode1
AGNet: Weighing Black Holes with Machine LearningCode1
A Sentinel-2 multi-year, multi-country benchmark dataset for crop classification and segmentation with deep learningCode1
AGNet: Weighing Black Holes with Deep LearningCode1
A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality ModelingCode1
Don't Pay Attention to the Noise: Learning Self-supervised Representations of Light Curves with a Denoising Time Series TransformerCode1
Dynamical Wasserstein Barycenters for Time-series ModelingCode1
Are we certain it's anomalous?Code1
ARMA Cell: A Modular and Effective Approach for Neural Autoregressive ModelingCode1
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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