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

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Showing 601–650 of 6748 papers

TitleStatusHype
Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration—0
Experimental demonstration of bandwidth enhancement in photonic time delay reservoir computing—0
Application of machine learning to gas flaring—0
Learnable Path in Neural Controlled Differential EquationsCode0
Spectral Cross-Domain Neural Network with Soft-adaptive Threshold Spectral EnhancementCode0
On Consistency and Asymptotic Normality of Least Absolute Deviation Estimators for 2-dimensional Sinusoidal Model—0
On the Susceptibility and Robustness of Time Series Models through Adversarial Attack and Defense—0
Machine Learning Applied to Peruvian Vegetables Imports—0
Generative Time Series Forecasting with Diffusion, Denoise, and DisentanglementCode2
Quantile Autoregression-based Non-causality Testing—0
A Robust Data-driven Process Modeling Applied to Time-series Stochastic Power Flow—0
Towards Long-Term Time-Series Forecasting: Feature, Pattern, and DistributionCode2
DANLIP: Deep Autoregressive Networks for Locally Interpretable Probabilistic Forecasting—0
Infomaxformer: Maximum Entropy Transformer for Long Time-Series Forecasting Problem—0
PMP: Privacy-Aware Matrix Profile against Sensitive Pattern Inference for Time SeriesCode0
Towards Explainable Land Cover Mapping: a Counterfactual-based Strategy—0
Recursive classification of satellite imaging time-series: An application to land cover mappingCode0
Graph state-space models—0
Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEGCode1
Common patterns between dengue cases, climate, and local environmental variables in Costa Rica: A Wavelet Approach—0
Identifying Exoplanets with Deep Learning. V. Improved Light Curve Classification for TESS Full Frame Image ObservationsCode0
Covariate-guided Bayesian mixture model for multivariate time seriesCode0
Neural SDEs for Conditional Time Series Generation and the Signature-Wasserstein-1 metricCode0
Non-intrusive Water Usage Classification Considering Limited Training Data—0
Explicitly Solvable Continuous-time Inference for Partially Observed Markov Processes—0
Follow the Timeline! Generating Abstractive and Extractive Timeline Summary in Chronological OrderCode1
Point Cloud-based Proactive Link Quality Prediction for Millimeter-wave Communications—0
Russia-Ukraine war: Modeling and Clustering the Sentiments Trends of Various Countries—0
Efficient Online Learning with Memory via Frank-Wolfe Optimization: Algorithms with Bounded Dynamic Regret and Applications to Control—0
Unleashing the Power of Shared Label Structures for Human Activity Recognition—0
A plug-in graph neural network to boost temporal sensitivity in fMRI analysis—0
A Functional approach for Two Way Dimension Reduction in Time Series—0
The Functional Wiener Filter—0
Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves—0
Label-Efficient Interactive Time-Series Anomaly Detection—0
Time series Forecasting to detect anomalous behaviours in Multiphase Flow Meters—0
Unsupervised 4D LiDAR Moving Object Segmentation in Stationary Settings with Multivariate Occupancy Time SeriesCode0
Behave-XAI: Deep Explainable Learning of Behavioral Representational Data—0
Deep Temporal Contrastive Clustering—0
Investigating Sindy As a Tool For Causal Discovery In Time Series Signals—0
End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based ReconciliationCode2
Robustifying Markowitz—0
AER: Auto-Encoder with Regression for Time Series Anomaly DetectionCode3
Semi-supervised multiscale dual-encoding method for faulty traffic data detection—0
Anomaly detection in laser-guided vehicles' batteries: a case study—0
Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies—0
Modeling Time-Series and Spatial Data for Recommendations and Other Applications—0
Multi-step-ahead Stock Price Prediction Using Recurrent Fuzzy Neural Network and Variational Mode DecompositionCode1
Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning—0
Deep Latent State Space Models for Time-Series GenerationCode1
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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