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 626–650 of 6748 papers

TitleStatusHype
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