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 2326–2350 of 6748 papers

TitleStatusHype
Deep Learning for Energy Markets—0
Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks—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
An End-to-End Model for Time Series Classification In the Presence of Missing Values—0
Deep learning delay coordinate dynamics for chaotic attractors from partial observable data—0
Deep Learning-Based Vehicle Speed Prediction for Ecological Adaptive Cruise Control in Urban and Highway Scenarios—0
Automated Model Selection for Time-Series Anomaly Detection—0
Deep Learning-based Time-varying Channel Estimation for RIS Assisted Communication—0
Deep learning based sferics recognition for AMT data processing in the dead band—0
Automated Mobility Context Detection with Inertial Signals—0
Adversarial Attacks on Multivariate Time Series—0
Deep learning-based sequential pattern mining for progressive database—0
Deep Learning Based on Generative Adversarial and Convolutional Neural Networks for Financial Time Series Predictions—0
Automated Machine Learning on Big Data using Stochastic Algorithm Tuning—0
Automated Label Generation for Time Series Classification with Representation Learning: Reduction of Label Cost for Training—0
Deep Learning based Covert Attack Identification for Industrial Control Systems—0
Deep Learning Architectures for FSCV, a Comparison—0
Automated Few-Shot Time Series Forecasting based on Bi-level Programming—0
An Empirical Study of the L2-Boost technique with Echo State Networks—0
A Comparison of Nineteen Various Electricity Consumption Forecasting Approaches and Practicing to Five Different Households in Turkey—0
Deep Learning Approaches for Forecasting Strawberry Yields and Prices Using Satellite Images and Station-Based Soil Parameters—0
Deep Learning Alternative to Explicit Model Predictive Control for Unknown Nonlinear Systems—0
Automated Diagnosis of Epilepsy Employing Multifractal Detrended Fluctuation Analysis Based Features—0
Deep learning algorithm for data-driven simulation of noisy dynamical system—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