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 5301–5325 of 6748 papers

TitleStatusHype
Deep State Space Models for Time Series Forecasting—0
Learning a Warping Distance from Unlabeled Time Series Using Sequence Autoencoders—0
Multivariate Time Series Imputation with Generative Adversarial Networks—0
Data-driven Air Quality Characterisation for Urban Environments: a Case Study—0
Deep Multimodal Learning: An Effective Method for Video Classification—0
Anomaly Detection Models for IoT Time Series Data—0
Deep Factors with Gaussian Processes for Forecasting—0
ADSaS: Comprehensive Real-time Anomaly Detection System—0
Improving Hospital Mortality Prediction with Medical Named Entities and Multimodal Learning—0
Recurrent Deep Divergence-based Clustering for simultaneous feature learning and clustering of variable length time series—0
A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics—0
Leveraging Clinical Time-Series Data for Prediction: A Cautionary Tale—0
Deep Haar Scattering Networks in Pattern Recognition: A promising approach—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
Deep Ensemble Tensor Factorization for Longitudinal Patient Trajectories Classification—0
Temporal Convolutional Neural Network for the Classification of Satellite Image Time SeriesCode0
Seasonal Stochastic Volatility and the Samuelson Effect in Agricultural Futures Markets—0
Multivariate Forecasting of Crude Oil Spot Prices using Neural Networks—0
Black-Box Autoregressive Density Estimation for State-Space Models—0
Multiple-Instance Learning by Boosting Infinitely Many Shapelet-based Classifiers—0
Coupled Recurrent Models for Polyphonic Music Composition—0
T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular SamplingCode0
Entropy and Transfer Entropy: The Dow Jones and the build up to the 1997 Asian Crisis—0
Fading of collective attention shapes the evolution of linguistic variants—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