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

Name: Forecasting multivariate time-series data using LSTM and mini-batches
Author: Khodabakhsh, Athar, Arı, İsmail, Bakır, M., Alagoz, S. M.

Title	Forecasting multivariate time-series data using LSTM and mini-batches
Author	Khodabakhsh, Athar, Arı, İsmail, Bakır, M., Alagoz, S. M.
Publication Date:	2020
Publication Place	- Springer
Subject	LSTM, Multivariate time-series, RNN, Sensors, Sequence data, Time-series
Type	Book
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	2367-4512
Record ID	f370f48d-9c22-4c93-b18b-47f9dea62c2e
Library Location	Computer Science
Date	2020
Sample Text	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.
Editör	Bohlouli, M., Bigham, B. S., Narimani, Z., Vasighi, M., Ansari, E.
DOI	10.1007/978-3-030-37309-2_10
Cilt	45

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Özyeğin University

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

Author Khodabakhsh, Athar, Arı, İsmail, Bakır, M., Alagoz, S. M.

Publication Date 2020

Publication Place - Springer

Subject LSTM, Multivariate time-series, RNN, Sensors, Sequence data, Time-series

Type Book

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 2367-4512

Record ID f370f48d-9c22-4c93-b18b-47f9dea62c2e

Library Location Computer Science

Date 2020

Sample Text 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.

Editör Bohlouli, M., Bigham, B. S., Narimani, Z., Vasighi, M., Ansari, E.

DOI 10.1007/978-3-030-37309-2_10

Cilt 45