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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 4501–4550 of 6748 papers

TitleStatusHype
Multi-Scale RCNN Model for Financial Time-series Classification—0
A Comparative Analysis of Forecasting Financial Time Series Using ARIMA, LSTM, and BiLSTM—0
Robust Inference on Infinite and Growing Dimensional Time Series Regression—0
A Scrambled Method of Moments—0
A simulation of the insurance industry: The problem of risk model homogeneity—0
Cell Line Classification Using Electric Cell-substrate Impedance Sensing (ECIS)—0
Discovering Subdimensional Motifs of Different Lengths in Large-Scale Multivariate Time SeriesCode0
Challenges with Extreme Class-Imbalance and Temporal Coherence: A Study on Solar Flare Data—0
Shapelets for earthquake detection—0
The dynamics of the stomatognathic system from 4D multimodal data—0
Object-based multi-temporal and multi-source land cover mapping leveraging hierarchical class relationshipsCode0
Equivariant online predictions of non-stationary time series—0
Learning Weighted Submanifolds with Variational Autoencoders and Riemannian Variational Autoencoders—0
Action Recognition Using Volumetric Motion RepresentationsCode0
Driver Identification Based on Vehicle Telematics Data using LSTM-Recurrent Neural NetworkCode0
Bayesian Filtering for Multi-period Mean-Variance Portfolio Selection—0
DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data—0
Temporal Knowledge Graph Embedding Model based on Additive Time Series DecompositionCode1
Bayesian Recurrent Framework for Missing Data Imputation and Prediction with Clinical Time Series—0
Benchmarking time series classification -- Functional data vs machine learning approachesCode0
Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-AttentionCode0
Comparison of Deep learning models on time series forecasting : a case study of Dissolved Oxygen PredictionCode0
Weather event severity prediction using buoy data and machine learning—0
The Creation and Validation of Load Time Series for Synthetic Electric Power Systems—0
RSM-GAN: A Convolutional Recurrent GAN for Anomaly Detection in Contaminated Seasonal Multivariate Time Series—0
Missingness as Stability: Understanding the Structure of Missingness in Longitudinal EHR data and its Impact on Reinforcement Learning in HealthcareCode0
SMART: Skeletal Motion Action Recognition aTtack—0
Deep learning for clustering of multivariate clinical patient trajectories with missing valuesCode0
Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China—0
Bayesian nonparametric discontinuity designCode0
A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis—0
Modelling EHR timeseries by restricting feature interaction—0
Synthetic Event Time Series Health Data Generation—0
Robust Parameter-Free Season Length Detection in Time SeriesCode0
Performance evaluation of deep neural networks for forecasting time-series with multiple structural breaks and high volatilityCode0
Real-Time Anomaly Detection for Advanced Manufacturing: Improving on Twitter's State of the Art—0
Self-supervised representation learning from electroencephalography signalsCode0
Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning—0
Time-Dynamic Estimates of the Reliability of Deep Semantic Segmentation NetworksCode0
Generating an Explainable ECG Beat Space With Variational Auto-Encoders—0
Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural NetworksCode0
Building Effective Large-Scale Traffic State Prediction System: Traffic4cast Challenge SolutionCode0
Making Good on LSTMs' Unfulfilled Promise—0
Time2Graph: Revisiting Time Series Modeling with Dynamic ShapeletsCode0
Modeling EEG data distribution with a Wasserstein Generative Adversarial Network to predict RSVP EventsCode0
SeismoGen: Seismic Waveform Synthesis Using Generative Adversarial Networks—0
Early Predictions for Medical Crowdfunding: A Deep Learning Approach Using Diverse Inputs—0
DeVLearn: A Deep Visual Learning Framework for Localizing Temporary Faults in Power Systems—0
XceptionTime: A Novel Deep Architecture based on Depthwise Separable Convolutions for Hand Gesture ClassificationCode0
Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems—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