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 1126–1150 of 6748 papers

TitleStatusHype
Automated Model Selection for Time-Series Anomaly Detection—0
Automated Mobility Context Detection with Inertial Signals—0
Adversarial Attacks on Multivariate Time Series—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
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
Topological Data Analysis of Task-Based fMRI Data from Experiments on Schizophrenia—0
Automated Diagnosis of Epilepsy Employing Multifractal Detrended Fluctuation Analysis Based Features—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
An empirical study of neural networks for trend detection in time series—0
A Comparison of Model-Free and Model Predictive Control for Price Responsive Water Heaters—0
An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks—0
Clustering Time Series with Nonlinear Dynamics: A Bayesian Non-Parametric and Particle-Based Approach—0
Structural clustering of volatility regimes for dynamic trading strategies—0
Automated Antenna Testing Using Encoder-Decoder-based Anomaly Detection—0
An Empirical Study on How the Developers Discussed about Pandas Topics—0
Autoencoding Time Series for Visualisation—0
Adversarial attacks against Bayesian forecasting dynamic models—0
A Bayesian Long Short-Term Memory Model for Value at Risk and Expected Shortfall Joint Forecasting—0
Cluster Variational Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data—0
CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation—0
Autoencoding Conditional GAN for Portfolio Allocation Diversification—0
Autoencoder-based time series clustering with energy applications—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