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.

İsim	Forecasting multivariate time-series data using LSTM and mini-batches
Yazar	Khodabakhsh, Athar, Arı, İsmail, Bakır, M., Alagoz, S. M.
Basım Tarihi:	2020
Basım Yeri	- Springer
Konu	LSTM, Multivariate time-series, RNN, Sensors, Sequence data, Time-series
Tür	Kitap
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	2367-4512
Kayıt Numarası	f370f48d-9c22-4c93-b18b-47f9dea62c2e
Lokasyon	Computer Science
Tarih	2020
Örnek Metin	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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Forecasting multivariate time-series data using LSTM and mini-batches

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

Basım Tarihi 2020

Basım Yeri - Springer

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

Tür Kitap

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 2367-4512

Kayıt Numarası f370f48d-9c22-4c93-b18b-47f9dea62c2e

Lokasyon Computer Science

Tarih 2020

Örnek Metin 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