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 2901–2925 of 6748 papers

TitleStatusHype
Generating Student Feedback from Time-Series Data Using Reinforcement Learning—0
Generating Trading Signals by ML algorithms or time series ones?—0
Generation of a Supervised Classification Algorithm for Time-Series Variable Stars with an Application to the LINEAR Dataset—0
Generation of Synthetic Multi-Resolution Time Series Load Data—0
Generative adversarial network based on chaotic time series—0
Functional Connectivity Dynamics show Resting-State Instability and Rightward Parietal Dysfunction in ADHD—0
Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecasting—0
Generative Adversarial Networks for Financial Trading Strategies Fine-Tuning and Combination—0
Kernel Hypothesis Testing with Set-valued Data—0
Generative Adversarial Networks in finance: an overview—0
Functional Classwise Principal Component Analysis: A Novel Classification Framework—0
Functional Bayesian Filter—0
Generative Modeling of Hidden Functional Brain Networks—0
Generative modeling of spatio-temporal weather patterns with extreme event conditioning—0
Functional Annotation of Human Cognitive States using Graph Convolution Networks—0
Conditional independence testing with a single realization of a multivariate nonstationary nonlinear time series—0
Generative Pre-Trained Transformer for Cardiac Abnormality Detection—0
A Robust Score-Driven Filter for Multivariate Time Series—0
Generative Time-series Modeling with Fourier Flows—0
A Modified Dynamic Time Warping (MDTW) Approach and Innovative Average Non-Self Match Distance (ANSD) Method for Anomaly Detection in ECG Recordings—0
Gene Regulatory Network Inference with Latent Force Models—0
Generic Tracking and Probabilistic Prediction Framework and Its Application in Autonomous Driving—0
Fully Learnable Deep Wavelet Transform for Unsupervised Monitoring of High-Frequency Time Series—0
Conditional heteroskedasticity in crypto-asset returns—0
Fully convolutional networks for structural health monitoring through multivariate time series classification—0
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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