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Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices

Author

Listed:
  • Lorenzo Menculini

    (Idea-re S.r.l., 06128 Perugia, Italy)

  • Andrea Marini

    (Idea-re S.r.l., 06128 Perugia, Italy)

  • Massimiliano Proietti

    (Idea-re S.r.l., 06128 Perugia, Italy)

  • Alberto Garinei

    (Idea-re S.r.l., 06128 Perugia, Italy
    Department of Engineering Sciences, Guglielmo Marconi University, 00193 Rome, Italy)

  • Alessio Bozza

    (Cancelloni Food Service S.p.A., 06063 Magione, Italy)

  • Cecilia Moretti

    (Independent Researcher, Via Parco 6, 06073 Corciano, Italy)

  • Marcello Marconi

    (Idea-re S.r.l., 06128 Perugia, Italy
    Department of Engineering Sciences, Guglielmo Marconi University, 00193 Rome, Italy)

Abstract

Setting sale prices correctly is of great importance for firms, and the study and forecast of prices time series is therefore a relevant topic not only from a data science perspective but also from an economic and applicative one. In this paper, we examine different techniques to forecast sale prices applied by an Italian food wholesaler, as a step towards the automation of pricing tasks usually taken care by human workforce. We consider ARIMA models and compare them to Prophet, a scalable forecasting tool by Facebook based on a generalized additive model, and to deep learning models exploiting Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNNs). ARIMA models are frequently used in econometric analyses, providing a good benchmark for the problem under study. Our results indicate that ARIMA models and LSTM neural networks perform similarly for the forecasting task under consideration, while the combination of CNNs and LSTMs attains the best overall accuracy, but requires more time to be tuned. On the contrary, Prophet is quick and easy to use, but considerably less accurate.

Suggested Citation

  • Lorenzo Menculini & Andrea Marini & Massimiliano Proietti & Alberto Garinei & Alessio Bozza & Cecilia Moretti & Marcello Marconi, 2021. "Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices," Forecasting, MDPI, vol. 3(3), pages 1-19, September.
    • Handle: RePEc:gam:jforec:v:3:y:2021:i:3:p:40-662:d:636189
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    References listed on IDEAS

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    6. Tendai Makoni & Delson Chikobvu, 2023. "Evaluating and Predicting the Long-Term Impact of the COVID-19 Pandemic on Manufacturing Sales within South Africa," Sustainability, MDPI, vol. 15(12), pages 1-18, June.

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