Testing Variants of LSTM Networks for a Production Forecasting Problem

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.