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 1651–1675 of 6748 papers

TitleStatusHype
Data-driven Residual Generation for Early Fault Detection with Limited Data—0
Data-driven soiling detection in PV modules—0
Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining—0
Data-driven Stabilization of Discrete-time Control-affine Nonlinear Systems: A Koopman Operator Approach—0
Data-Driven Time Series Reconstruction for Modern Power Systems Research—0
Data Exploration and Validation on dense knowledge graphs for biomedical research—0
Data-Folding and Hyperspace Coding for Multi-Dimensonal Time-Series Data Imaging—0
A Model Combining Convolutional Neural Network and LightGBM Algorithm for Ultra-Short-Term Wind Power Forecasting—0
Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting—0
Data manipulation detection via permutation information theory quantifiers—0
Deep Learning with Convolutional Neural Network for Objective Skill Evaluation in Robot-assisted Surgery—0
Data Quality Over Quantity: Pitfalls and Guidelines for Process Analytics—0
Dataset Bias in Human Activity Recognition—0
Deep Learning with Long Short-Term Memory for Time Series Prediction—0
A Transfer-Learning Based Ensemble Architecture for ECG Signal Classification—0
Deep Multimodal Learning: An Effective Method for Video Classification—0
Data Smashing 2.0: Sequence Likelihood (SL) Divergence For Fast Time Series Comparison—0
Data-Space Inversion Using a Recurrent Autoencoder for Time-Series Parameterization—0
Deep Neural Networks on EEG signals to predict Attention Score using Gramian Angular Difference Field—0
Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling—0
Day-ahead time series forecasting: application to capacity planning—0
Day Level Forecasting for Coronavirus Disease (COVID-19) Spread: Analysis, Modeling and Recommendations—0
DBT-DMAE: An Effective Multivariate Time Series Pre-Train Model under Missing Data—0
Concentration inequalities for correlated network-valued processes with applications to community estimation and changepoint analysis—0
Concealer: SGX-based Secure, Volume Hiding, and Verifiable Processing of Spatial Time-Series Datasets—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