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 2476–2500 of 6748 papers

TitleStatusHype
Day-ahead time series forecasting: application to capacity planning—0
A transformer-based model for default prediction in mid-cap corporate markets—0
An Artificial Neural Network-based Stock Trading System Using Technical Analysis and Big Data Framework—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
DATSING: Data Augmented Time Series Forecasting with Adversarial Domain Adaptation—0
Data-Space Inversion Using a Recurrent Autoencoder for Time-Series Parameterization—0
Data Smashing 2.0: Sequence Likelihood (SL) Divergence For Fast Time Series Comparison—0
A Transformer-based Deep Learning Algorithm to Auto-record Undocumented Clinical One-Lung Ventilation Events—0
An Applied Deep Learning Approach for Estimating Soybean Relative Maturity from UAV Imagery to Aid Plant Breeding Decisions—0
A Transfer-Learning Based Ensemble Architecture for ECG Signal Classification—0
Dataset Bias in Human Activity Recognition—0
A Trainable Reconciliation Method for Hierarchical Time-Series—0
An Anomaly Detection Method for Satellites Using Monte Carlo Dropout—0
A Direct Estimation of High Dimensional Stationary Vector Autoregressions—0
A Comparative Study of the Clinical use of Motion Analysis from Kinect Skeleton Data—0
A Bayesian Ensemble for Unsupervised Anomaly Detection—0
Data-driven model reduction, Wiener projections, and the Koopman-Mori-Zwanzig formalism—0
Data Quality Over Quantity: Pitfalls and Guidelines for Process Analytics—0
A topological analysis of cointegrated data: a Z24 Bridge case study—0
Data manipulation detection via permutation information theory quantifiers—0
Dirichlet Proportions Model for Hierarchically Coherent Probabilistic Forecasting—0
An analysis of deep neural networks for predicting trends in time series data—0
Data-Folding and Hyperspace Coding for Multi-Dimensonal Time-Series Data Imaging—0
Data Exploration and Validation on dense knowledge graphs for biomedical research—0
Data-Driven Time Series Reconstruction for Modern Power Systems Research—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