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

• URL: http://arxiv.org/abs/2107.12770v1
• Date: Fri, 23 Jul 2021 15:13:31 GMT
• Title: Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices
• Authors: Lorenzo Menculini, Andrea Marini, Massimiliano Proietti, Alberto Garinei, Alessio Bozza, Cecilia Moretti, Marcello Marconi
• Abstract summary: We study different techniques to forecast the sale prices of three food products applied by an Italian food wholesaler. We consider ARIMA models and compare them to Prophet, a scalable forecasting tool developed by Facebook. Our results indicate that ARIMA performs similarly to LSTM neural networks for the problem under study, while the combination of CNNs and LSTMs attains the best overall accuracy, but requires more time to be tuned.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 exhamine different techniques to forecast the sale prices of three food products 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 developed by Facebook and based on a generalized additive model, and to deep learning models based on Long Short--Term Memory (LSTM) and Convolutional Neural Networks (CNNs). ARIMA models are frequently used in econometric analyses, providing a good bechmark for the problem under study. Our results indicate that ARIMA performs similarly to LSTM neural networks for the problem under study, while the combination of CNNs and LSTMs attains the best overall accuracy, but requires more time to be tuned. On the contrary, Prophet is very fast to use, but less accurate.

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