Developing a multivariate time series forecasting framework based on stacked autoencoders and multi-phase feature

Developing a multivariate time series forecasting framework based on stacked autoencoders and multi-phase feature

Time series forecasting across different domains has received massive attention as it eases intelligent decision-making activities. Recurrent neural networks and various deep learning algorithms have been applied to modeling and forecasting multivariate time series data. Due to intricate non-linear...

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Bibliographic Details
Main Authors: Dilip Kumar Sharma, Ravi Prakash Varshney, Saurabh Agarwal, Amel Ali Alhussan, Hanaa A. Abdallah
Format: Article
Language:English
Published: Elsevier 2024-04-01
Series:Heliyon
Subjects:
Feature selection
Autoencoder
Deep learning
Long short-term memory (LSTM)
Convolutional neural network (CNN)
Multivariate time series forecasting (MTS)
Online Access:http://www.sciencedirect.com/science/article/pii/S240584402403891X

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http://www.sciencedirect.com/science/article/pii/S240584402403891X

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