Can I read Performance Analysis of Long Short-Term Memory Predictive Neural Networks on Time Series Data on EtoBox?

Performance Analysis of Long Short-Term Memory Predictive Neural Networks on Time Series Data by Roland Bolboacă; Piroska Haller is a Mathematics article available to read on EtoBox.

What is Performance Analysis of Long Short-Term Memory Predictive Neural Networks on Time Series Data about?

Long short-term memory neural networks have been proposed as a means of creating accurate models from large time series data originating from various fields. These models can further be utilized for prediction, control, or anomaly-detection algorithms. However, finding the optimal hyperparameters to maximize different performance criteria remains a challenge for both novice and experienced users. Hyperparameter optimization algorithms can often be a resource-intensive and time-consuming task, particularly when the impact of the hyperparameters on the performance of the neural network is not comprehended or known. Teacher forcing denotes a procedure that involves feeding the ground truth output from the previous time-step as input to the current time-step during training, while during testing feeding back the predicted values. This paper presents a comprehensive examination of the impact of hyperparameters on long short-term neural networks, with and without teacher forcing, on prediction performance. The study includes testing long short-term memory neural networks, with two variations of teacher forcing, in two prediction modes, using two configurations (i.e., multi-input single-o

Who reads Performance Analysis of Long Short-Term Memory Predictive Neural Networks on Time Series Data?

Author

Roland Bolboacă; Piroska Haller

Publisher

MDPI AG

Published

2023

Language

EN

Field

Mathematics (Physical Sciences)