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 1326–1350 of 6748 papers

TitleStatusHype
Machine Learning Approach and Extreme Value Theory to Correlated Stochastic Time Series with Application to Tree Ring Data—0
Learning the Dynamics of Sparsely Observed Interacting SystemsCode0
Targeted Attacks on Timeseries Forecasting—0
Learning Informative Representation for Fairness-aware Multivariate Time-series Forecasting: A Group-based PerspectiveCode0
Effect of temporal resolution on the reproduction of chaotic dynamics via reservoir computing—0
Better than DFA? A Bayesian Method for Estimating the Hurst Exponent in Behavioral Sciences—0
Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text—0
Coincident Learning for Unsupervised Anomaly Detection—0
Causal Structural Learning from Time Series: A Convex Optimization Approach—0
Causal Graph Discovery from Self and Mutually Exciting Time Series—0
Recursive deep learning framework for forecasting the decadal world economic outlookCode0
Motion ID: Human Authentication Approach—0
Partial Mobilization: Tracking Multilingual Information Flows Amongst Russian Media Outlets and Telegram—0
Parameterizing the cost function of Dynamic Time Warping with application to time series classification—0
Multi-view Kernel PCA for Time series Forecasting—0
Context-specific kernel-based hidden Markov model for time series analysisCode0
Neuronal architecture extracts statistical temporal patternsCode0
A Framework for Evaluating the Impact of Food Security Scenarios—0
ECGAN: Self-supervised generative adversarial network for electrocardiography—0
Learning Reservoir Dynamics with Temporal Self-Modulation—0
LSTM and CNN application for core-collapse supernova search in gravitational wave real data—0
Chemical Integration of ODEs using Idealized Abstract SolutionsCode0
Flexible conditional density estimation for time series—0
Estimation of Sea State Parameters from Ship Motion Responses Using Attention-based Neural Networks—0
The Conditional Cauchy-Schwarz Divergence with Applications to Time-Series Data and Sequential Decision MakingCode0
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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