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Forecasting Multivariate Time-Series Data Using LSTM and Mini-Batches

Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT,volume 45)


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.


  • LSTM
  • Multivariate time-series
  • RNN
  • Sensors
  • Sequence data
  • Time-series

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  • DOI: 10.1007/978-3-030-37309-2_10
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This research was sponsored by a grant from TÜPRAŞ (Turkish Petroleum Refineries Inc.) R&D group. We would like to thank Burak Aydoğan and Mehmet Aydin for collecting and providing us with sensor data.

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Correspondence to Athar Khodabakhsh .

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Khodabakhsh, A., Ari, I., Bakır, M., Alagoz, S.M. (2020). Forecasting Multivariate Time-Series Data Using LSTM and Mini-Batches. In: Bohlouli, M., Sadeghi Bigham, B., Narimani, Z., Vasighi, M., Ansari, E. (eds) Data Science: From Research to Application. CiDaS 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 45. Springer, Cham.

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