Testing Variants of LSTM Networks for a Production Forecasting Problem

Nouf Alkaabi, Sid Shakya, Rabeb Mizouni

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

    Abstract

    Forecasting the production of essential items such as food is one of the issues that many retail authorities encounter frequently. A well-planned supply chain will prevent an under-and an oversupply. By forecasting behaviors and trends using historical data and other accessible parameters, AI-driven demand forecasting techniques can address this problem. Earlier work has focused on the traditional Machine Learning (ML) models, such as Auto-Regression (AR), Auto-regressive Integrated Moving Average (ARIMA), and Long Short-Term Memory (LSTM) for forecasting production. A thorough experimental analysis demonstrates that various models can perform better in various datasets. However, with additional hyper-parameters that may be further tweaked to increase accuracy, the LSTM technique is typically the most adaptable. In this work, we explore the possibility of incorporating additional non-sequential features with the view of increasing the accuracy of the forecast. For this, the month of production, temperature, and the number of rainy days are considered as additional static non-sequential features. There are various ways such static features can be incorporated in a sequential model such as LSTM. In this work, two variants are built, and their performances for the problem of food production forecasting are compared.

    Original languageBritish English
    Title of host publicationProceedings of the 15th International Joint Conference on Computational Intelligence, IJCCI 2023
    EditorsNiki van Stein, Francesco Marcelloni, H. K. Lam, Marie Cottrell, Joaquim Filipe
    Pages524-531
    Number of pages8
    ISBN (Electronic)9789897586743
    DOIs
    StatePublished - 2023
    Event15th International Joint Conference on Computational Intelligence, IJCCI 2023 - Hybrid, Rome, Italy
    Duration: 13 Nov 202315 Nov 2023

    Publication series

    NameInternational Joint Conference on Computational Intelligence
    ISSN (Electronic)2184-3236

    Conference

    Conference15th International Joint Conference on Computational Intelligence, IJCCI 2023
    Country/TerritoryItaly
    CityHybrid, Rome
    Period13/11/2315/11/23

    Keywords

    • Forecasting
    • LSTM
    • Non-Sequential Features
    • Sequential Features
    • Time Series Analysis

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