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 901–925 of 6748 papers

TitleStatusHype
Quantifying Quality of Class-Conditional Generative Models in Time-Series Domain—0
Estimation of High-Dimensional Markov-Switching VAR Models with an Approximate EM Algorithm—0
Latent Temporal Flows for Multivariate Analysis of Wearables Data—0
LEAVES: Learning Views for Time-Series Data in Contrastive Learning—0
A Large-Scale Annotated Multivariate Time Series Aviation Maintenance Dataset from the NGAFIDCode1
Marginalized particle Gibbs for multiple state-space models coupled through shared parameters—0
Early Discovery of Disappearing Entities in Microblogs—0
Anomaly detection in dynamic networksCode0
Data augmentation on-the-fly and active learning in data stream classificationCode0
Empirical Evaluation of Data Augmentations for Biobehavioral Time Series Data with Deep LearningCode1
Entropy Approximation by Machine Learning Regression: Application for Irregularity Evaluation of Images in Remote Sensing—0
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
Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series ForecastingCode1
Deep Counterfactual Estimation with Categorical Background VariablesCode1
Short-term prediction of stream turbidity using surrogate data and a meta-model approach—0
Combining datasets to increase the number of samples and improve model fittingCode0
Class-Specific Explainability for Deep Time Series ClassifiersCode0
Energy-Efficient Deployment of Machine Learning Workloads on Neuromorphic Hardware—0
Self-explaining Hierarchical Model for Intraoperative Time SeriesCode0
Mining Causality from Continuous-time Dynamics Models: An Application to Tsunami Forecasting—0
A Clustering Algorithm for Correlation Quickest Hub Discovery Mixing Time Evolution and Random Matrix Theory—0
Multi-Task Dynamical SystemsCode0
ANFIS-based prediction of power generation for combined cycle power plant—0
Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts—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