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 776–800 of 6748 papers

TitleStatusHype
InceptionTime: Finding AlexNet for Time Series ClassificationCode1
Recurrent Trend Predictive Neural Network for Multi-Sensor Fire DetectionCode1
RED: Deep Recurrent Neural Networks for Sleep EEG Event DetectionCode1
DTAAD: Dual Tcn-Attention Networks for Anomaly Detection in Multivariate Time Series DataCode1
Global RTK Positioning in Graphical State SpaceCode1
Remaining Useful Life Estimation Under Uncertainty with Causal GraphNetsCode1
Attention based Multi-Modal New Product Sales Time-series ForecastingCode1
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systemsCode1
Dynamic Data Augmentation with Gating Networks for Time Series RecognitionCode1
Attention-Based Deep Learning Framework for Human Activity Recognition with User AdaptationCode1
Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time SeriesCode1
Dynamic Slate Recommendation with Gated Recurrent Units and Thompson SamplingCode1
Robust Factorization of Real-world Tensor Streams with Patterns, Missing Values, and OutliersCode1
Attention to Warp: Deep Metric Learning for Multivariate Time SeriesCode1
Robust Probabilistic Time Series ForecastingCode1
Attentive Neural Controlled Differential Equations for Time-series Classification and ForecastingCode1
Early Abandoning and Pruning for Elastic Distances including Dynamic Time WarpingCode1
Generalised Interpretable Shapelets for Irregular Time SeriesCode1
Generalization of Change-Point Detection in Time Series Data Based on Direct Density Ratio EstimationCode1
EEG-Inception: An Accurate and Robust End-to-End Neural Network for EEG-based Motor Imagery ClassificationCode1
EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signalsCode1
A Transformer-based Framework for Multivariate Time Series Representation LearningCode1
General Evaluation for Instruction Conditioned Navigation using Dynamic Time WarpingCode1
FusionLane: Multi-Sensor Fusion for Lane Marking Semantic Segmentation Using Deep Neural NetworksCode1
Generative Network-Based Reduced-Order Model for Prediction, Data Assimilation and Uncertainty QuantificationCode1
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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