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 1651–1675 of 6748 papers

TitleStatusHype
Fast Saturating Gate for Learning Long Time Scales with Recurrent Neural Networks—0
MTSMAE: Masked Autoencoders for Multivariate Time-Series Forecasting—0
Public Transit Arrival Prediction: a Seq2Seq RNN Approach—0
Nonparametric and Regularized Dynamical Wasserstein Barycenters for Sequential Observations—0
Connecting Surrogate Safety Measures to Crash Probablity via Causal Probabilistic Time Series Prediction—0
Using Entropy Measures for Monitoring the Evolution of Activity Patterns—0
Improving Convolutional Neural Networks for Fault Diagnosis by Assimilating Global Features—0
Combined Dynamic Virtual Spatiotemporal Graph Mapping for Traffic PredictionCode0
Review of Clustering Methods for Functional Data—0
Grouped self-attention mechanism for a memory-efficient Transformer—0
Fast and Robust Video-Based Exercise Classification via Body Pose Tracking and Scalable Multivariate Time Series ClassifiersCode0
Solar Power Time Series Forecasting Utilising Wavelet Coefficients—0
Robust Trajectory-based Density Estimation for Geometric Structure Recovery: Theory and Applications—0
A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring—0
A Sequence-Aware Recommendation Method Based on Complex Networks—0
Fast Inference for Quantile Regression with Tens of Millions of Observations—0
A case study of spatiotemporal forecasting techniques for weather forecastingCode0
Happy or grumpy? A Machine Learning Approach to Analyze the Sentiment of Airline Passengers' Tweets—0
Non-contrastive representation learning for intervals from well logs—0
Explainable classification of astronomical uncertain time series—0
Masked Multi-Step Multivariate Time Series Forecasting with Future Information—0
Towards Automatic Forecasting: Evaluation of Time-Series Forecasting Models for Chickenpox Cases Estimation in Hungary—0
Active Transfer Prototypical Network: An Efficient Labeling Algorithm for Time-Series Data—0
Experimental study of time series forecasting methods for groundwater level predictionCode0
Predicting Swarm Equatorial Plasma Bubbles via Machine Learning and Shapley Values—0
Show:102550
← PrevPage 67 of 270Next →
All datasetsPhysioNet Challenge 2012Speech CommandsVentilator Pressure Prediction

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