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 1751–1800 of 6748 papers

TitleStatusHype
A metric to compare the anatomy variation between image time series—0
Deep Gaussian Covariance Network—0
A Data-Driven Approach for Predicting Vegetation-Related Outages in Power Distribution Systems—0
Comprehensive Review of Neural Differential Equations for Time Series Analysis—0
ARISE: ApeRIodic SEmi-parametric Process for Efficient Markets without Periodogram and Gaussianity Assumptions—0
Deep Generative SToRM model for dynamic imaging—0
Deep Generators on Commodity Markets; application to Deep Hedging—0
Autoencoding Conditional GAN for Portfolio Allocation Diversification—0
Accurate shape and phase averaging of time series through Dynamic Time Warping—0
Composition Properties of Inferential Privacy for Time-Series Data—0
Deep Hedging, Generative Adversarial Networks, and Beyond—0
Autoencoding Time Series for Visualisation—0
Evaluating the Planning and Operational Resilience of Electrical Distribution Systems with Distributed Energy Resources using Complex Network Theory—0
Deep Hedging under Rough Volatility—0
Deep Imputation of Missing Values in Time Series Health Data: A Review with Benchmarking—0
Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection—0
DeStress: Deep Learning for Unsupervised Identification of Mental Stress in Firefighters from Heart-rate Variability (HRV) Data—0
An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks—0
A review on outlier/anomaly detection in time series data—0
Interpretable Deep Representation Learning from Temporal Multi-view Data—0
Deep learning algorithm for data-driven simulation of noisy dynamical system—0
Automated Detection of Left Ventricle in Arterial Input Function Images for Inline Perfusion Mapping using Deep Learning: A study of 15,000 Patients—0
Deep Learning Alternative to Explicit Model Predictive Control for Unknown Nonlinear Systems—0
Deep Learning Approaches for Forecasting Strawberry Yields and Prices Using Satellite Images and Station-Based Soil Parameters—0
Composite FORCE learning of chaotic echo state networks for time-series prediction—0
Deep Learning Architectures for FSCV, a Comparison—0
Deep Learning based Covert Attack Identification for Industrial Control Systems—0
A Method for Massively Parallel Analysis of Time Series—0
Composable Generative Models—0
Complex-valued Gaussian Process Regression for Time Series Analysis—0
Deep Learning Based on Generative Adversarial and Convolutional Neural Networks for Financial Time Series Predictions—0
Deep learning-based sequential pattern mining for progressive database—0
A review on distance based time series classification—0
Deep Learning-based Time-varying Channel Estimation for RIS Assisted Communication—0
Deep Learning-Based Vehicle Speed Prediction for Ecological Adaptive Cruise Control in Urban and Highway Scenarios—0
Deep learning delay coordinate dynamics for chaotic attractors from partial observable data—0
Deep Learning Enables Reduced Gadolinium Dose for Contrast-Enhanced Blood-Brain Barrier Opening—0
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network—0
Deep Learning for Energy Markets—0
Deep Learning for Energy Time-Series Analysis and Forecasting—0
A Data-Driven Approach for Modeling Stochasticity in Oil Market—0
Detailed Primary and Secondary Distribution System Model Enhancement Using AMI Data—0
Automated Testing of AI Models—0
Deep Learning for Multi-Scale Changepoint Detection in Multivariate Time Series—0
Deteção de estruturas permanentes a partir de dados de séries temporais Sentinel 1 e 2—0
Complex systems: features, similarity and connectivity—0
Deep Learning for Plasma Tomography and Disruption Prediction from Bolometer Data—0
Inferring Global Dynamics of a Black-Box System Using Machine Learning—0
Complex systems approach to natural language—0
A Review on Deep Learning in UAV Remote Sensing—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