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 1601–1625 of 6748 papers

TitleStatusHype
Universal hidden monotonic trend estimation with contrastive learning—0
Research of an optimization model for servicing a network of ATMs and information payment terminals—0
Layer-wise Relevance Propagation for Echo State Networks applied to Earth System Variability—0
Optimal Event Monitoring through Internet Mashup over Multivariate Time Series—0
Modelling Emotion Dynamics in Song Lyrics with State Space Models—0
Temporal-Spatial dependencies ENhanced deep learning model (TSEN) for household leverage series forecasting—0
tegdet: An extensible Python Library for Anomaly Detection using Time-Evolving GraphsCode0
Flipped Classroom: Effective Teaching for Time Series ForecastingCode0
From time-series transcriptomics to gene regulatory networks: a review on inference methods—0
Extreme-Long-short Term Memory for Time-series Prediction—0
Quantifying Quality of Class-Conditional Generative Models in Time-Series Domain—0
Bandwidth-efficient distributed neural network architectures with application to body sensor networks—0
Latent Temporal Flows for Multivariate Analysis of Wearables Data—0
Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems—0
An Empirical Evaluation of Multivariate Time Series Classification with Input Transformation across Different DimensionsCode0
Convolutional Neural Networks: Basic Concepts and Applications in Manufacturing—0
Estimation of High-Dimensional Markov-Switching VAR Models with an Approximate EM Algorithm—0
Entropy Approximation by Machine Learning Regression: Application for Irregularity Evaluation of Images in Remote Sensing—0
LEAVES: Learning Views for Time-Series Data in Contrastive Learning—0
Data augmentation on-the-fly and active learning in data stream classificationCode0
Early Discovery of Disappearing Entities in Microblogs—0
Marginalized particle Gibbs for multiple state-space models coupled through shared parameters—0
Anomaly detection in dynamic networksCode0
Deterioration Prediction using Time-Series of Three Vital Signs and Current Clinical Features Amongst COVID-19 Patients—0
Multi-Content Time-Series Popularity Prediction with Multiple-Model Transformers in MEC Networks—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