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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

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Showing 5051–5100 of 6748 papers

TitleStatusHype
Time-Series Analysis via Low-Rank Matrix Factorization Applied to Infant-Sleep Data—0
Handling temporality of clinical events with application to Adverse Drug Event detection in Electronic Health Records: A scoping review—0
Enhancing Time Series Momentum Strategies Using Deep Neural NetworksCode0
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
Fitting stochastic predator-prey models using both population density and kill rate dataCode0
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
Decomposing Temperature Time Series with Non-Negative Matrix Factorization—0
Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks—0
A Novel GAN-based Fault Diagnosis Approach for Imbalanced Industrial Time Series—0
On the interplay between multiscaling and stocks dependence—0
Voltage Quality Time Series Classification using Convolutional Neural Network—0
Two-phase flow regime prediction using LSTM based deep recurrent neural network—0
On Arrhythmia Detection by Deep Learning and Multidimensional Representation—0
Asymptotic nonparametric statistical analysis of stationary time series—0
The maximum entropy mortality model: forecasting mortality using statistical moments Codes—0
RAPID: Early Classification of Explosive Transients using Deep Learning—0
Probabilistic Forecasting of Sensory Data with Generative Adversarial Networks - ForGANCode0
Few-Shot Deep Adversarial Learning for Video-based Person Re-identification—0
Bayesian prediction of jumps in large panels of time series data—0
Medical Time Series Classification with Hierarchical Attention-based Temporal Convolutional Networks: A Case Study of Myotonic Dystrophy Diagnosis—0
Smoothing Nonlinear Variational Objectives with Sequential Monte Carlo—0
Improved Dynamic Time Warping (DTW) Approach for Online Signature Verification—0
Cross-Modal Data Programming Enables Rapid Medical Machine Learning—0
Weak Supervision for Time Series: Wearable Sensor Classification with Limited Labeled Data—0
Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models—0
Time Series Imputation—0
Optimal Combination Forecasts on Retail Multi-Dimensional Sales Data—0
A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes—0
Forecasting, Causality, and Impulse Response with Neural Vector Autoregressions—0
Learning Disentangled Representations of Satellite Image Time Series—0
Trainable Time Warping: Aligning Time-Series in the Continuous-Time DomainCode0
Multi-Task Time Series Analysis applied to Drug Response Modelling—0
Dynamic Hurst Exponent in Time Series—0
State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual PredictionCode0
Random Pairwise Shapelets ForestCode0
Adversarial Attacks on Deep Neural Networks for Time Series ClassificationCode0
tspDB: Time Series Predict DB—0
Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators—0
Multi-Stage Fault Warning for Large Electric Grids Using Anomaly Detection and Machine Learning—0
Deep Neural Network Ensembles for Time Series ClassificationCode0
Deep Switch Networks for Generating Discrete Data and Language—0
ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling—0
Forecasting Spatio-Temporal Renewable Scenarios: a Deep Generative Approach—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