Comparing Prophet and Deep Learning to ARIMA in Forecasting Wholesale Food Prices
نویسندگان
چکیده
Setting sale prices correctly is of great importance for firms, and the study forecast time series therefore a relevant topic not only from data science perspective but also an economic applicative one. In this paper, we examine different techniques to applied by Italian food wholesaler, as step towards automation pricing tasks usually taken care human workforce. We consider ARIMA models compare them Prophet, scalable forecasting tool Facebook based on generalized additive model, deep learning exploiting Long Short-Term Memory (LSTM) Convolutional Neural Networks (CNNs). are frequently used in econometric analyses, providing good benchmark problem under study. Our results indicate that LSTM neural networks perform similarly task consideration, while combination CNNs LSTMs attains best overall accuracy, requires more be tuned. On contrary, Prophet quick easy use, considerably less accurate.
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ژورنال
عنوان ژورنال: Forecasting
سال: 2021
ISSN: ['2571-9394']
DOI: https://doi.org/10.3390/forecast3030040