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 5101–5125 of 6748 papers

TitleStatusHype
Enhancing Time Series Momentum Strategies Using Deep Neural NetworksCode0
Time-Series Analysis via Low-Rank Matrix Factorization Applied to Infant-Sleep Data—0
RecovDB: accurate and efficient missing blocks recovery for large time seriesCode0
A Statistical Investigation of Long Memory in Language and MusicCode0
CRAD: Clustering with Robust Autocuts and DepthCode0
Human Intracranial EEG Quantitative Analysis and Automatic Feature Learning for Epileptic Seizure Prediction—0
Short note on the behavior of recurrent neural network for noisy dynamical system—0
Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection—0
Decomposing Temperature Time Series with Non-Negative Matrix Factorization—0
Online Topology Identification from Vector Autoregressive Time SeriesCode0
SSIM -A Deep Learning Approach for Recovering Missing Time Series Sensor DataCode0
Automatic alignment of surgical videos using kinematic dataCode0
Fitting stochastic predator-prey models using both population density and kill rate dataCode0
A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series—0
Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks—0
On the interplay between multiscaling and stocks dependence—0
Asymptotic nonparametric statistical analysis of stationary time series—0
On Arrhythmia Detection by Deep Learning and Multidimensional Representation—0
Two-phase flow regime prediction using LSTM based deep recurrent neural network—0
Voltage Quality Time Series Classification using Convolutional Neural Network—0
Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGANCode0
Few-Shot Deep Adversarial Learning for Video-based Person Re-identification—0
The maximum entropy mortality model: forecasting mortality using statistical moments Codes—0
RAPID: Early Classification of Explosive Transients using Deep Learning—0
Bayesian prediction of jumps in large panels of time series data—0
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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