A Multi-Section Hierarchical Deep Neural Network Model for Time Series Classification: Applied To Wearable Sensor-Based Human Activity Recognition
Z Ghorrati, A Esmaeili, ET Matson - IEEE Access, 2024 - ieeexplore.ieee.org
Time Series Classification, is one of the very challenging areas in pattern recognition
problems. As the volume of time series data increases, a multitude of TSC algorithms have …
problems. As the volume of time series data increases, a multitude of TSC algorithms have …
Hyperparameter Optimization of Long Short Term Memory Models for Interpretable Electrical Fault Classification
The reliability of the model significantly affects early detection and accurate classification of
electrical faults. In this study, a Long Short Term Memory based fault classification model …
electrical faults. In this study, a Long Short Term Memory based fault classification model …
Design of an Iterative Dual Metaheuristic VARMAx Model Enhancing Efficiency of Time Series Predictions
Y Boddu, A Manimaran, B Arunkumar… - IEEE Access, 2024 - ieeexplore.ieee.org
Traditional methods, while effective to a degree, often grapple with limitations such as
reduced accuracy, specificity, and higher prediction delays, particularly in high-stakes fields …
reduced accuracy, specificity, and higher prediction delays, particularly in high-stakes fields …
A Method Based on Lie Group Machine Learning for Multivariate Time-Series Clustering
Y Huang, X Luo - 2024 6th International Conference on Data …, 2024 - ieeexplore.ieee.org
A multivariate time series (MTS) is a data series formed from observations of multiple
variables at multiple time points, which may exhibit interdependencies and temporal …
variables at multiple time points, which may exhibit interdependencies and temporal …
Combining static and time series data using attention mechanisms for forecasting and classification.
H Krijt - 2024 - studenttheses.uu.nl
Utilizing both static and time series data can enhance the performance of machine learning
models. However, existing methods of concatenating data lead to high dimensionality and …
models. However, existing methods of concatenating data lead to high dimensionality and …