Evolving Deep CNN-LSTMs for Inventory Time Series Prediction

Ning Xue, Isaac Triguero, Grazziela P. Figueredo, Dario Landa-Silva

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

39 Citations (Scopus)


Inventory forecasting is a key component of effective inventory management. In this work, we utilise hybrid deep learning models for inventory forecasting. According to the highly nonlinear and non-stationary characteristics of inventory data, the models employ Long Short-Term Memory (LSTM) to capture long temporal dependencies and Convolutional Neural Network (CNN) to learn the local trend features. However, designing optimal CNN-LSTM network architecture and tuning parameters can be challenging and would require consistent human supervision. To automate optimal architecture searching of CNN-LSTM, we implement three meta-heuristics: a Particle Swarm Optimisation (PSO) and two Differential Evolution (DE) variants. Computational experiments on real-world inventory forecasting problems are conducted to evaluate the performance of the applied meta-heuristics in terms of evolved network architectures for obtaining prediction accuracy. Moreover, the evolved CNN-LSTM models are also compared to Seasonal Auto-regressive Integrated Moving Average (SARIMA) models for inventory forecasting problems. The experimental results indicate that the evolved CNN-LSTM models are capable of dealing with complex nonlinear inventory forecasting problem.

Original languageEnglish
Title of host publication2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages8
ISBN (Electronic)9781728121536
Publication statusPublished - Jun 2019
Externally publishedYes
Event2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Wellington, New Zealand
Duration: 10 Jun 201913 Jun 2019

Publication series

Name2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings


Conference2019 IEEE Congress on Evolutionary Computation, CEC 2019
Country/TerritoryNew Zealand


  • Convolutional Neural Network
  • Differential Evolution
  • Inventory Prediction
  • Long Short-Term Memory
  • Particle Swarm Optimisation
  • Time Series Analysis

ASJC Scopus subject areas

  • Computational Mathematics
  • Modelling and Simulation


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