Publication:
Forecasting multivariate time-series data using LSTM and mini-batches

Placeholder

Institution Authors

Research Projects

Organizational Unit

Journal Title

Journal ISSN

Volume Title

Type

Book chapter

Access

info:eu-repo/semantics/restrictedAccess

Publication Status

Published

Journal Issue

Abstract

Multivariate time-series data forecasting is a challenging task due to nonlinear interdependencies in complex industrial systems. It is crucial to model these dependencies automatically using the ability of neural networks to learn features by extraction of spatial relationships. In this paper, we converted non-spatial multivariate time-series data into a time-space format and used Recurrent Neural Networks (RNNs) which are building blocks of Long Short-Term Memory (LSTM) networks for sequential analysis of multi-attribute industrial data for future predictions. We compared the effect of mini-batch length and attribute numbers on prediction accuracy and found the importance of spatio-temporal locality for detecting patterns using LSTM.

Date

2020

Publisher

Springer

Description

Keywords

Citation

Collections


Page Views

0

File Download

0