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Found 10 papers, 0 papers with code
Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance
In this study, we leverage SCADA data from diverse wind turbines to predict power output, employing advanced time series methods, specifically Functional Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks.
Predictive Analysis for Optimizing Port Operations
This research addresses a significant gap in port analysis models for vessel Stay and Delay times, offering a valuable contribution to the field of maritime logistics.
Optimal Load Shedding for Public Safety Power Shutoffs
This approach will help utilities to effectively manage PSPS events and reduce the risk of wildfires caused by the power lines.
An ensemble of convolution-based methods for fault detection using vibration signals
This paper focuses on solving a fault detection problem using multivariate time series of vibration signals collected from planetary gearboxes in a test rig.
A Functional approach for Two Way Dimension Reduction in Time Series
The rise in data has led to the need for dimension reduction techniques, especially in the area of non-scalar variables, including time series, natural language processing, and computer vision.
Modern Non-Linear Function-on-Function Regression
We introduce a new class of non-linear function-on-function regression models for functional data using neural networks.
Non-linear Functional Modeling using Neural Networks
We introduce a new class of non-linear models for functional data based on neural networks.
Modern Multiple Imputation with Functional Data
This work considers the problem of fitting functional models with sparsely and irregularly sampled functional data.
A Non-linear Function-on-Function Model for Regression with Time Series Data
Specifically, we aim to learn mathematical mappings from multiple chronologically measured numerical variables within a certain time interval S to multiple numerical variables of interest over time interval T. Prior arts, including the multivariate regression model, the Seq2Seq model, and the functional linear models, suffer from several limitations.
Spatio-Temporal Functional Neural Networks
Explosive growth in spatio-temporal data and its wide range of applications have attracted increasing interests of researchers in the statistical and machine learning fields.
