A scalable approach based on deep learning for big data time series forecasting

J. F. Torres; A. Galicia; Alicia Troncoso; Francisco Martínez‐Álvarez

doi:10.3233/ica-180580

August 14, 2018Integrated Computer-Aided Engineering

A scalable approach based on deep learning for big data time series forecasting

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Key Points

To develop a scalable forecasting framework capable of handling big data time series using deep learning and distributed processing.
Implemented deep feedforward neural networks using the H2O big data analysis framework.
Integrated neural network modeling with Apache Spark to facilitate distributed data processing and training across computing clusters.
Demonstrated the functional deployment of deep feedforward networks for large-scale time series prediction tasks.
Established distributed computational scalability for processing high-volume temporal datasets without localized hardware bottlenecks.

Implication

Computational study demonstrates scalable time series forecasting in big data frameworks, indicating the efficiency of distributed deep learning architectures.

Abstract

This paper presents a method based on deep learning to deal with big data times series forecasting. The deep feed forward neural network provided by the H2O big data analysis framework has been used along with the Apache Spark platform for distribute

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Cite This Study

Torres et al. (2018) studied this question.

synapsesocial.com/papers/6a16b62c7cba52b0f77b8233 https://doi.org/10.3233/ica-180580

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A scalable approach based on deep learning for big data time series forecasting

Key Points

Implication

Abstract

Discussion

Cite This Study

A scalable approach based on deep learning for big data time series forecasting

Key Points

Implication

Abstract

Discussion

Cite This Study

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