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

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Showing 5951–6000 of 6748 papers

TitleStatusHype
Dalek -- a deep-learning emulator for TARDIS—0
CSformer: Combining Channel Independence and Mixing for Robust Multivariate Time Series Forecasting—0
DancingLines: An Analytical Scheme to Depict Cross-Platform Event Popularity—0
DANLIP: Deep Autoregressive Networks for Locally Interpretable Probabilistic Forecasting—0
DANNTe: a case study of a turbo-machinery sensor virtualization under domain shift—0
DANTE: A framework for mining and monitoring darknet traffic—0
Darts: User-Friendly Modern Machine Learning for Time Series—0
DASKT: A Dynamic Affect Simulation Method for Knowledge Tracing—0
Data Anomaly Detection for Structural Health Monitoring of Bridges using Shapelet Transform—0
Data Augmentation for Multivariate Time Series Classification: An Experimental Study—0
Data Augmentation techniques in time series domain: A survey and taxonomy—0
Data Curves Clustering Using Common Patterns Detection—0
Data-driven Air Quality Characterisation for Urban Environments: a Case Study—0
Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems—0
Data-Driven Construction of Data Center Graph of Things for Anomaly Detection—0
Data-Driven Failure Prediction in Brittle Materials: A Phase-Field Based Machine Learning Framework—0
Data-Driven Forecasting of High-Dimensional Chaotic Systems with Long Short-Term Memory Networks—0
Data-driven forecasting of solar irradiance—0
Data-Driven Forecast of Dengue Outbreaks in Brazil: A Critical Assessment of Climate Conditions for Different Capitals—0
Data-driven Identification and Prediction of Power System Dynamics Using Linear Operators—0
Data-driven Influence Based Clustering of Dynamical Systems—0
Data-Driven Interaction Analysis of Line Failure Cascading in Power Grid Networks—0
Data-Driven Learning of the Number of States in Multi-State Autoregressive Models—0
Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System—0
Data-driven mapping between functional connectomes using optimal transport—0
Data-driven Neural Architecture Learning For Financial Time-series Forecasting—0
Data-Driven Fault Diagnosis Analysis and Open-Set Classification of Time-Series Data—0
Data-driven prediction and control of extreme events in a chaotic flow—0
Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps—0
Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models—0
Data-driven Real-time Short-term Prediction of Air Quality: Comparison of ES, ARIMA, and LSTM—0
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
Data manipulation detection via permutation information theory quantifiers—0
Data Quality Over Quantity: Pitfalls and Guidelines for Process Analytics—0
Dataset Bias in Human Activity Recognition—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
DATSING: Data Augmented Time Series Forecasting with Adversarial Domain Adaptation—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
DDPG based on multi-scale strokes for financial time series trading strategy—0
De Bruijn goes Neural: Causality-Aware Graph Neural Networks for Time Series Data on Dynamic Graphs—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