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 5251–5275 of 6748 papers

TitleStatusHype
An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data—0
Predicting Blood Pressure Response to Fluid Bolus Therapy Using Attention-Based Neural Networks for Clinical Interpretability—0
Modeling Irregularly Sampled Clinical Time SeriesCode0
Examining Deep Learning Architectures for Crime Classification and Prediction—0
Improving Clinical Predictions through Unsupervised Time Series Representation Learning—0
Imputation of Clinical Covariates in Time Series—0
Data-driven Air Quality Characterisation for Urban Environments: a Case Study—0
Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders—0
Multiple Instance Learning for Efficient Sequential Data Classification on Resource-constrained Devices—0
Learning filter widths of spectral decompositions with waveletsCode0
Multivariate Time Series Imputation with Generative Adversarial Networks—0
Deep State Space Models for Time Series Forecasting—0
Extracting Relationships by Multi-Domain MatchingCode0
Anomaly Detection Models for IoT Time Series Data—0
Deep Factors with Gaussian Processes for Forecasting—0
Deep Multimodal Learning: An Effective Method for Video Classification—0
ADSaS: Comprehensive Real-time Anomaly Detection System—0
Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale—0
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning—0
Deep Haar Scattering Networks in Pattern Recognition: A promising approach—0
A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics—0
Recurrent Deep Divergence-based Clustering for simultaneous feature learning and clustering of variable length time series—0
Multi-step Time Series Forecasting Using Ridge Polynomial Neural Network with Error-Output Feedbacks—0
Lagged correlation-based deep learning for directional trend change prediction in financial time series—0
Temporal Convolutional Neural Network for the Classification of Satellite Image Time SeriesCode0
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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