Home Appliance Demand Forecasting: A Comparative Approach Using Traditional and Machine Learning Algorithms

Lissette Culcay, Fernanda Bustillos, Diego Vallejo-Huanga

Producción científica: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

Resumen

The manufacturing industry is considered one of Ecuador’s most important productive sectors because it is an excellent employment and national income source. Durable consumer goods such as white and brown goods have shown an increase with a positive trend on their GDP, so there is an expectation of growth in the market in the following years. The profitability of this industry depends on various internal factors, such as supply chain management, and external factors, such as market dynamics, which subsequently allow for generating demand forecasts. This scientific article uses sales data from an Ecuadorian white goods manufacturer company to forecast demand in two production lines. The KDD methodology was used for data processing and model construction. Three classic forecasting methods were used in the experimentation: Simple Moving Average, Simple Exponential Smoothing, and ARIMA, and three forecasting methods that use artificial intelligence algorithms: Random Forest, K-Nearest Neighbors, and Artificial Neural Networks. The performance of the forecast models was evaluated using four error metrics: MSE, MAE, RMSE, and MASE. The first experiment considered all the observations in the dataset, while for the second experiment, the dataset was partitioned into training and test sections for cross-validation. Based on the results of error metrics, ARIMA is the best-performing model for the classic algorithms and Random Forest for the Machine Learning models. Machine Learning models generally show a superior performance of up to 30% compared to classical forecasting methods to generate demand forecasts for household appliances.

Idioma originalInglés
Título de la publicación alojadaIntelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 3
EditoresKohei Arai
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas457-473
Número de páginas17
ISBN (versión impresa)9783031477140
DOI
EstadoPublicada - 2024
EventoIntelligent Systems Conference, IntelliSys 2023 - Amsterdam, Países Bajos
Duración: 7 sep. 20238 sep. 2023

Serie de la publicación

NombreLecture Notes in Networks and Systems
Volumen824 LNNS

Conferencia

ConferenciaIntelligent Systems Conference, IntelliSys 2023
País/TerritorioPaíses Bajos
CiudadAmsterdam
Período7/09/238/09/23

Nota bibliográfica

Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Huella

Profundice en los temas de investigación de 'Home Appliance Demand Forecasting: A Comparative Approach Using Traditional and Machine Learning Algorithms'. En conjunto forman una huella única.

Citar esto