Time Series Forecasting in Retail Sales Using LSTM and Prophet

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Author(s): Clony Junior (IEETA, University of Aveiro, Portugal), Pedro Gusmão (IEETA, University of Aveiro, Portugal), José Moreira (DETI, University of Aveiro, Portugal)and Ana Maria M. Tome (DETI, University of Aveiro, Portugal)
Copyright: 2021
Pages: 22
Source title: Handbook of Research on Applied Data Science and Artificial Intelligence in Business and Industry
Source Author(s)/Editor(s): Valentina Chkoniya (University of Aveiro, Portugal)
DOI: 10.4018/978-1-7998-6985-6.ch011

Keywords: Artificial Intelligence / Data Management / Information Science Reference / Knowledge Management and Library Science

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Abstract

Data science highlights fields of study and research such as time series, which, although widely explored in the past, gain new perspectives in the context of this discipline. This chapter presents two approaches to time series forecasting, long short-term memory (LSTM), a special kind of recurrent neural network (RNN), and Prophet, an open-source library developed by Facebook for time series forecasting. With a focus on developing forecasting processes by data mining or machine learning experts, LSTM uses gating mechanisms to deal with long-term dependencies, reducing the short-term memory effect inherent to the traditional RNN. On the other hand, Prophet encapsulates statistical and computational complexity to allow broad use of time series forecasting, prioritizing the expert's business knowledge through exploration and experimentation. Both approaches were applied to a retail time series. This case study comprises daily and half-hourly forecasts, and the performance of both methods was measured using the standard metrics.

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Time Series Forecasting in Retail Sales Using LSTM and Prophet

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