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 5101–5150 of 6748 papers

TitleStatusHype
Efficient Optimization of Echo State Networks for Time Series DatasetsCode1
Graph Hierarchical Convolutional Recurrent Neural Network (GHCRNN) for Vehicle Condition Prediction—0
Confident Kernel Sparse Coding and Dictionary Learning—0
Deep Learning in Asset Pricing—0
Financial Trading Model with Stock Bar Chart Image Time Series with Deep Convolutional Neural Networks—0
SleepNet: Automated Sleep Analysis via Dense Convolutional Neural Network Using Physiological Time Series—0
Revisiting clustering as matrix factorisation on the Stiefel manifold—0
Towards Time-Aware Distant Supervision for Relation Extraction—0
Should we Reload Time Series Classification Performance Evaluation ? (a position paper)—0
An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves—0
GRATIS: GeneRAting TIme Series with diverse and controllable characteristicsCode0
Learning the dynamics of technical trading strategiesCode1
Temporal Registration in Application to In-utero MRI Time Series—0
Robust Lane Detection from Continuous Driving Scenes Using Deep Neural NetworksCode0
Autoregressive Convolutional Recurrent Neural Network for Univariate and Multivariate Time Series PredictionCode0
Denoising Gravitational Waves with Enhanced Deep Recurrent Denoising Auto-Encoders—0
Data-driven Neural Architecture Learning For Financial Time-series Forecasting—0
Multiple-Kernel Dictionary Learning for Reconstruction and Clustering of Unseen Multivariate Time-series—0
Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting—0
Making the Dynamic Time Warping Distance Warping-Invariant—0
Time Series Source Separation using Dynamic Mode DecompositionCode0
3D Graph Convolutional Networks with Temporal Graphs: A Spatial Information Free Framework For Traffic Forecasting—0
Financial series prediction using Attention LSTM—0
On Robustness of Principal Component Regression—0
A Model Combining Convolutional Neural Network and LightGBM Algorithm for Ultra-Short-Term Wind Power Forecasting—0
Reducing Artificial Neural Network Complexity: A Case Study on Exoplanet Detection—0
Adversarial Attacks on Time SeriesCode1
Insights into LSTM Fully Convolutional Networks for Time Series ClassificationCode1
Self-Organization in Spontaneous Movements of Neonates generates Self-specifying Sensory Experiences—0
Deep MR Fingerprinting with total-variation and low-rank subspace priors—0
Robust and Subject-Independent Driving Manoeuvre Anticipation through Domain-Adversarial Recurrent Neural NetworksCode0
Fused Lasso for Feature Selection using Structural Information—0
Market efficiency, liquidity, and multifractality of Bitcoin: A dynamic study—0
Short-term Road Traffic Prediction based on Deep Cluster at Large-scale Networks—0
Forecasting intracranial hypertension using multi-scale waveform metrics—0
E-LSTM-D: A Deep Learning Framework for Dynamic Network Link PredictionCode0
Fault Diagnosis Method Based on Scaling Law for On-line Refrigerant Leak Detection—0
Deep Adaptive Input Normalization for Time Series ForecastingCode1
Convolutional-Sparse-Coded Dynamic Mode Decomposition and Its Application to River State Estimation—0
Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI—0
Approximating Continuous Functions on Persistence Diagrams Using Template FunctionsCode0
Structural Recurrent Neural Network for Traffic Speed PredictionCode1
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection—0
Robust and fast heart rate variability analysis of long and noisy electrocardiograms using neural networks and images—0
Learning to Adaptively Scale Recurrent Neural Networks—0
KINN: Incorporating Expert Knowledge in Neural Networks—0
Actions Generation from Captions—0
Quick and Easy Time Series Generation with Established Image-based GANs—0
The Many-to-Many Mapping Between the Concordance Correlation Coefficient and the Mean Square ErrorCode0
Sinkhorn Divergence of Topological Signature Estimates for Time Series ClassificationCode0
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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