Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data

Salvatore Carta, Andrea Medda, Alessio Pili, Diego Reforgiato Recupero and Roberto Saia
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Salvatore Carta: Department of Mathematics and Computer Science, University of Cagliari, Palazzo delle Scienze, Via Ospedale 72, 09124 Cagliari, Italy
Andrea Medda: Department of Mathematics and Computer Science, University of Cagliari, Palazzo delle Scienze, Via Ospedale 72, 09124 Cagliari, Italy
Alessio Pili: Department of Mathematics and Computer Science, University of Cagliari, Palazzo delle Scienze, Via Ospedale 72, 09124 Cagliari, Italy
Diego Reforgiato Recupero: Department of Mathematics and Computer Science, University of Cagliari, Palazzo delle Scienze, Via Ospedale 72, 09124 Cagliari, Italy
Roberto Saia: Department of Mathematics and Computer Science, University of Cagliari, Palazzo delle Scienze, Via Ospedale 72, 09124 Cagliari, Italy

Future Internet, 2018, vol. 11, issue 1, 1-19

Abstract: E-commerce is becoming more and more the main instrument for selling goods to the mass market. This led to a growing interest in algorithms and techniques able to predict products future prices, since they allow us to define smart systems able to improve the quality of life by suggesting more affordable goods and services. The joint use of time series, reputation and sentiment analysis clearly represents one important approach to this research issue. In this paper we present Price Probe, a suite of software tools developed to perform forecasting on products’ prices. Its primary aim is to predict the future price trend of products generating a customized forecast through the exploitation of autoregressive integrated moving average (ARIMA) model. We experimented the effectiveness of the proposed approach on one of the biggest E-commerce infrastructure in the world: Amazon. We used specific APIs and dedicated crawlers to extract and collect information about products and their related prices over time and, moreover, we extracted information from social media and Google Trends that we used as exogenous features for the ARIMA model. We fine-estimated ARIMA’s parameters and tried the different combinations of the exogenous features and noticed through experimental analysis that the presence of Google Trends information significantly improved the predictions.

Keywords: smart systems; time-series forecasting; ARIMA; machine learning; Amazon; Google Trends (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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