A Transcendental LASSO Function for Combining Machine Learning and Statistical Model Forecasts

UÄŸur Åžener and Salvatore Joseph Terregrossa

SAGE Open, 2024, vol. 14, issue 3, 21582440241262695

Abstract: The aim of the study is the development of methodology for accurate estimation of electric vehicle demand; which is paramount regarding various aspects of the firms decision-making such as optimal price, production level, and corresponding amounts of capital and labor; as well as supply chain, inventory control, capital financing, and operational expenses management. The forecasting methods utilized include statistical techniques (autoregressive integrated moving average [ARIMA], and polynomial regression), machine learning (nonlinear autoregressive neural network [NAR]), deep learning (long short-term memory [LSTM]), hybrid and combination forecasting . With regard to the latter method, our study experiments with four different combining model approaches, including the introduction of an original, novel combining method with the employment of a transcendental LASSO function , which is used to form combinations of forecasts generated by the NAR, ARIMA , and polynomial regression models. The LASSO -based combining model proved superior to all other models, for the majority of forecast error statistics; where the root mean square error (RMSE) and mean absolute percentage error (MAPE) values are 4.5% and 8% respectively lower than the average level of the component model forecasts. The major implications of our empirical findings are that greater accuracy in demand forecasting can be achieved with a combining model approach, rather than reliance on any particular, singular model. Furthermore, given its superior performance, the employment of the studys LASSO -based combining model to forecast electric vehicle demand may lead to optimal firm decision-making over a range of organizational facets, which is predicated on accurate demand function estimation.

Keywords: artificial neural network; autoregressive integrated moving average; combination forecasting; generalized reduced gradient; hybrid forecasting; least absolute shrinkage and selection operator; long short-term memory; nonlinear autoregressive neural network; polynomial regression; weighted least squares (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:sae:sagope:v:14:y:2024:i:3:p:21582440241262695

DOI: 10.1177/21582440241262695

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