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 4401–4425 of 6748 papers

TitleStatusHype
Core-Collapse Supernova Gravitational-Wave Search and Deep Learning Classification—0
Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation—0
INFERENCE, PREDICTION, AND ENTROPY RATE OF CONTINUOUS-TIME, DISCRETE-EVENT PROCESSES—0
Improved Modeling of Complex Systems Using Hybrid Physics/Machine Learning/Stochastic Models—0
Evidence of disorientation towards immunization on online social media after contrasting political communication on vaccines. Results from an analysis of Twitter data in Italy—0
Interpretable Conservation Law Estimation by Deriving the Symmetries of Dynamics from Trained Deep Neural Networks—0
Grey Models for Short-Term Queue Length Predictions for Adaptive Traffic Signal Control—0
Use Short Isometric Shapelets to Accelerate Binary Time Series Classification—0
Interpreting LSTM Prediction on Solar Flare Eruption with Time-series ClusteringCode0
Crowdfunding Dynamics Tracking: A Reinforcement Learning Approach—0
Alpha Discovery Neural Network based on Prior Knowledge—0
Evolutionary Dynamics of Investors Expectations and Market Price Movement—0
A Gated Recurrent Unit Approach to Bitcoin Price Prediction—0
Visual Evaluation of Generative Adversarial Networks for Time Series Data—0
A Distributed Neural Network Architecture for Robust Non-Linear Spatio-Temporal Prediction—0
On the Initialization of Long Short-Term Memory Networks—0
Graph Message Passing with Cross-location Attentions for Long-term ILI Prediction—0
Monetary Policy and Wealth Inequalities in Great Britain: Assessing the role of unconventional policies for a decade of household data—0
Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation—0
"The Squawk Bot": Joint Learning of Time Series and Text Data Modalities for Automated Financial Information Filtering—0
Features or Shape? Tackling the False Dichotomy of Time Series Classification—0
Regularized Estimation of High-Dimensional Vector AutoRegressions with Weakly Dependent Innovations—0
Temporal Fusion Transformers for Interpretable Multi-horizon Time Series ForecastingCode3
Variable-lag Granger Causality for Time Series AnalysisCode1
Continuous Meta-Learning without TasksCode0
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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