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Evolving Deep CNN-LSTMs for Inventory Time Series Prediction

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

Authors

Ning Xue



Abstract

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 Autoregressive 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.

Citation

Xue, N., Triguero, I., Figueredo, G. P., & Landa-Silva, D. (2019). Evolving Deep CNN-LSTMs for Inventory Time Series Prediction. https://doi.org/10.1109/CEC.2019.8789957

Conference Name 2019 IEEE Congress on Evolutionary Computation (CEC)
Start Date Jun 10, 2019
End Date Jun 13, 2019
Acceptance Date Apr 8, 2019
Online Publication Date Jun 10, 2019
Publication Date Jun 1, 2019
Deposit Date Jan 16, 2020
Publisher Institute of Electrical and Electronics Engineers
Pages 1517-1524
ISBN 9781728121536
DOI https://doi.org/10.1109/CEC.2019.8789957
Public URL https://nottingham-repository.worktribe.com/output/2067694
Publisher URL https://ieeexplore.ieee.org/document/8789957
Additional Information © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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