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

Lorenzo Menculini, Andrea Marini, Massimiliano Proietti, Alberto Garinei, Alessio Bozza, Cecilia Moretti and Marcello Marconi
Additional contact information
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
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

Forecasting, 2021, vol. 3, issue 3, 1-19

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.

Keywords: time-series; forecasting; deep learning; ARIMA; prophet; prices; sales (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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