{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,5]],"date-time":"2026-04-05T23:28:31Z","timestamp":1775431711871,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2018,12,24]],"date-time":"2018-12-24T00:00:00Z","timestamp":1545609600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009873","name":"Regione Autonoma della Sardegna","doi-asserted-by":"publisher","award":["F19G14000910008"],"award-info":[{"award-number":["F19G14000910008"]}],"id":[{"id":"10.13039\/501100009873","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>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\u2019 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\u2019s 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.<\/jats:p>","DOI":"10.3390\/fi11010005","type":"journal-article","created":{"date-parts":[[2018,12,24]],"date-time":"2018-12-24T10:37:49Z","timestamp":1545647869000},"page":"5","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":50,"title":["Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data"],"prefix":"10.3390","volume":"11","author":[{"given":"Salvatore","family":"Carta","sequence":"first","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Cagliari, Palazzo delle Scienze, Via Ospedale 72, 09124 Cagliari, Italy"}]},{"given":"Andrea","family":"Medda","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Cagliari, Palazzo delle Scienze, Via Ospedale 72, 09124 Cagliari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9450-9793","authenticated-orcid":false,"given":"Alessio","family":"Pili","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Cagliari, Palazzo delle Scienze, Via Ospedale 72, 09124 Cagliari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8646-6183","authenticated-orcid":false,"given":"Diego","family":"Reforgiato Recupero","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Cagliari, Palazzo delle Scienze, Via Ospedale 72, 09124 Cagliari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1734-0437","authenticated-orcid":false,"given":"Roberto","family":"Saia","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, University of Cagliari, Palazzo delle Scienze, Via Ospedale 72, 09124 Cagliari, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2018,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zimmermann, S., Herrmann, P., Kundisch, D., and Nault, B. (2018). Decomposing the Variance of Consumer Ratings and the Impact on Price and Demand. Inf. Syst. Res.","DOI":"10.1287\/isre.2017.0764"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.eswa.2016.02.006","article-title":"Computational Intelligence and Financial Markets: A Survey and Future Directions","volume":"55","author":"Cavalcante","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"5932","DOI":"10.1016\/j.eswa.2008.07.006","article-title":"Surveying stock market forecasting techniques\u2014Part II: Soft computing methods","volume":"36","author":"Atsalakis","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.omega.2004.07.024","article-title":"A hybrid ARIMA and support vector machines model in stock price forecasting","volume":"33","author":"Pai","year":"2005","journal-title":"Omega"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1109\/TPWRS.2005.846054","article-title":"Day-ahead electricity price forecasting using the wavelet transform and ARIMA models","volume":"20","author":"Conejo","year":"2005","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_6","first-page":"981","article-title":"Application of ARIMA model for forecasting agricultural prices","volume":"19","author":"Jadhav","year":"2017","journal-title":"J. Agric. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1080\/2150704X.2017.1418992","article-title":"Short-term cloud coverage prediction using the ARIMA time series model","volume":"9","author":"Wang","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Castillo, O., Melin, P., and Kacprzyk, J. (2018). Comparative Study of ARIMA Methods for Forecasting Time Series of the Mexican Stock Exchange. Fuzzy Logic Augmentation of Neural and Optimization Algorithms: Theoretical Aspects and Real Applications, Springer.","DOI":"10.1007\/978-3-319-71008-2"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1080\/15567249.2017.1423413","article-title":"ARIMA forecasting of China\u2019s coal consumption, price and investment by 2030","volume":"13","author":"Jiang","year":"2018","journal-title":"Energy Sources Part B Econ. Plan. Policy"},{"key":"ref_10","first-page":"52","article-title":"Forecasting Energy Consumption of Turkey by Arima Model","volume":"8","author":"Ozturk","year":"2018","journal-title":"J. Asian Sci. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2938","DOI":"10.3390\/en7052938","article-title":"Autoregressive with exogenous variables and neural network short-term load forecast models for residential low voltage distribution networks","volume":"7","author":"Bennett","year":"2014","journal-title":"Energies"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"169","DOI":"10.18178\/ijmlc.2018.8.2.682","article-title":"E-Commerce Price Forecasting Using LSTM Neural Networks","volume":"8","author":"Bakir","year":"2018","journal-title":"Int. J. Mach. Learn. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, W.W., Liu, Y., and Chan, N.H. (2018). Modeling eBay Price Using Stochastic Differential Equations. J. Forecast.","DOI":"10.1002\/for.2551"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1080\/13504851.2011.613744","article-title":"Searching for the picture: forecasting UK cinema admissions using Google Trends data","volume":"19","author":"Hand","year":"2012","journal-title":"Appl. Econ. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1016\/j.tourman.2014.07.014","article-title":"Can Google data improve the forecasting performance of tourist arrivals? Mixed-data sampling approach","volume":"46","author":"Skeete","year":"2015","journal-title":"Tour. Manag."},{"key":"ref_16","unstructured":"Wei, W.W.S. (2006). Time Series Analysis: Univariate and Multivariate Methods, Pearson Addison Wesley."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Tyralis, H., and Papacharalampous, G. (2017). Variable selection in time series forecasting using random forests. Algorithms, 10.","DOI":"10.3390\/a10040114"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s40562-018-0111-1","article-title":"One-step ahead forecasting of geophysical processes within a purely statistical framework","volume":"5","author":"Papacharalampous","year":"2018","journal-title":"Geosci. Lett."},{"key":"ref_19","unstructured":"Meyler, A., Kenny, G., and Quinn, T. (1998). Forecasting Irish Inflation Using ARIMA Models, Central Bank of Ireland."},{"key":"ref_20","first-page":"361","article-title":"Time-series modelling and forecasting: Modelling of rainfall prediction using ARIMA model","volume":"8","author":"Geetha","year":"2016","journal-title":"Int. J. Soc. Syst. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Pincheira, P., and Hardy, N. (2018). Forecasting Base Metal Prices with Commodity Currencies, University Library of Munich. MPRA Paper 83564.","DOI":"10.2139\/ssrn.3095448"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"914","DOI":"10.1016\/j.ijforecast.2015.11.011","article-title":"Probabilistic electric load forecasting: A tutorial review","volume":"32","author":"Hong","year":"2016","journal-title":"Int. J. Forecast."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yu, L., Zhao, Y., Tang, L., and Yang, Z. (2018). Online big data-driven oil consumption forecasting with Google trends. Int. J. Forecast.","DOI":"10.1016\/j.ijforecast.2017.11.005"},{"key":"ref_24","unstructured":"Deokar, A.V., Gupta, A., Iyer, L.S., and Jones, M.C. (2018). The Competitive Landscape of Mobile Communications Industry in Canada: Predictive Analytic Modeling with Google Trends and Twitter. Analytics and Data Science: Advances in Research and Pedagogy, Springer."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bhalla, S., Bhateja, V., Chandavale, A.A., Hiwale, A.S., and Satapathy, S.C. (2018). Predictive Analysis of E-Commerce Products. Intelligent Computing and Information and Communication, Springer.","DOI":"10.1007\/978-981-10-7245-1"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/s10660-017-9272-9","article-title":"Price prediction of e-commerce products through Internet sentiment analysis","volume":"18","author":"Tseng","year":"2018","journal-title":"Electron. Commer. Res."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.physa.2016.11.114","article-title":"Can investor sentiment be used to predict the stock price? Dynamic analysis based on China stock market","volume":"469","author":"Guo","year":"2017","journal-title":"Phys. A Stat. Mech. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Brockwell, P.J., and Davis, R.A. (2016). Introduction. Introduction to Time Series and Forecasting, Springer.","DOI":"10.1007\/978-3-319-29854-2"},{"key":"ref_29","first-page":"427","article-title":"Distribution of the Estimators for Autoregressive Time Series with a Unit Root","volume":"74","author":"Dickey","year":"1979","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.2307\/1912517","article-title":"Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root","volume":"49","author":"Dickey","year":"1981","journal-title":"Econometrica"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1519","DOI":"10.1111\/1468-0262.00256","article-title":"Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power","volume":"69","author":"Ng","year":"2001","journal-title":"Econometrica"},{"key":"ref_32","unstructured":"Box, G.E.P., and Jenkins, G. (1990). Time Series Analysis, Forecasting and Control, Holden-Day, Incorporated."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1111\/bmsp.12109","article-title":"Testing autocorrelation and partial autocorrelation: Asymptotic methods versus resampling techniques","volume":"71","author":"Ke","year":"2018","journal-title":"Br. J. Math. Stat. Psychol."},{"key":"ref_34","unstructured":"Seabold, S., and Perktold, J. (July, January 28). Statsmodels: Econometric and statistical modeling with python. Proceedings of the 9th Python in Science Conference, Austin, TX, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1016\/j.jhydrol.2012.11.017","article-title":"Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir","volume":"476","author":"Valipour","year":"2013","journal-title":"J. Hydrol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1007\/s10586-017-0803-x","article-title":"Model and forecast stock market behavior integrating investor sentiment analysis and transaction data","volume":"20","author":"Zhang","year":"2017","journal-title":"Clust. Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1509\/jm.11.0011","article-title":"The effects of positive and negative online customer reviews: Do brand strength and category maturity matter?","volume":"77","author":"Carson","year":"2013","journal-title":"J. Mark."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1287\/isre.1120.0469","article-title":"Social media brand community and consumer behavior: Quantifying the relative impact of user-and marketer-generated content","volume":"24","author":"Goh","year":"2013","journal-title":"Inf. Syst. Res."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1287\/isre.2013.0512","article-title":"\u201cPopularity effect\u201d in user-generated content: Evidence from online product reviews","volume":"25","author":"Goes","year":"2014","journal-title":"Inf. Syst. Res."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kim, Y., and Srivastava, J. (2007, January 19\u201322). Impact of social influence in e-commerce decision making. Proceedings of the Ninth International Conference on Electronic Commerce, Minneapolis, MN, USA.","DOI":"10.1145\/1282100.1282157"},{"key":"ref_41","unstructured":"Gilbert, C.H.E. (2014, January 1\u20134). Vader: A parsimonious rule-based model for sentiment analysis of social media text. Proceedings of the Eighth International Conference on Weblogs and Social Media (ICWSM-14), Ann Arbor, MI, USA. Available online: http:\/\/comp. social. gatech.edu\/papers\/icwsm14.vader.hutto.pdf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1177\/0013164404272502","article-title":"A Comparison of Missing-Data Procedures for Arima Time-Series Analysis","volume":"65","author":"Velicer","year":"2005","journal-title":"Educ. Psychol. Meas."},{"key":"ref_43","unstructured":"Hyndman, R.J., and Athanasopoulos, G. (2018). Forecasting: Principles and Practice, OTexts."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1007\/s11600-018-0120-7","article-title":"Predictability of monthly temperature and precipitation using automatic time series forecasting methods","volume":"66","author":"Papacharalampous","year":"2018","journal-title":"Acta Geophys."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Hyndman, R.J., and Khandakar, Y. (2008). Automatic Time Series Forecasting: The forecast Package for R. J. Stat. Softw., 27.","DOI":"10.18637\/jss.v027.i03"},{"key":"ref_46","first-page":"91","article-title":"Some recent advances in forecasting and control","volume":"17","author":"Box","year":"1968","journal-title":"J. R. Stat. Soc. Ser. C (Appl. Stat.)"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"7067","DOI":"10.1016\/j.eswa.2012.01.039","article-title":"A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition","volume":"39","author":"Taieb","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_48","unstructured":"Khobai, H., and Chitauro, M. (2018, December 21). The Impact of Trade Liberalisation on Economic Growth in Switzerland. Available online: https:\/\/mpra.ub.uni-muenchen.de\/89884\/."},{"key":"ref_49","first-page":"103","article-title":"Non-stationary Gaussian ARFIMA processes: Estimation and application","volume":"22","author":"Lopes","year":"2002","journal-title":"Braz. Rev. Econom."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Flores, J.H.F., Engel, P.M., and Pinto, R.C. (2012, January 10\u201315). Autocorrelation and partial autocorrelation functions to improve neural networks models on univariate time series forecasting. Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia.","DOI":"10.1109\/IJCNN.2012.6252470"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/0169-2070(92)90008-W","article-title":"Error measures for generalizing about forecasting methods: Empirical comparisons","volume":"8","author":"Armstrong","year":"1992","journal-title":"Int. J. Forecast."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1016\/j.apenergy.2018.11.034","article-title":"A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting","volume":"235","author":"Yang","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1007\/s40092-016-0165-7","article-title":"Development of an evolutionary fuzzy expert system for estimating future behavior of stock price","volume":"13","author":"Mehmanpazir","year":"2017","journal-title":"J. Ind. Eng. Int."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.neucom.2015.12.114","article-title":"Mean Absolute Percentage Error for regression models","volume":"192","author":"Golden","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.jspi.2018.07.005","article-title":"Optimality of training\/test size and resampling effectiveness in cross-validation","volume":"199","author":"Afendras","year":"2019","journal-title":"J. Stat. Plan. Inference"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/07350015.2014.983236","article-title":"Comparing Predictive Accuracy, Twenty Years Later: A Personal Perspective on the Use and Abuse of Diebold\u2013Mariano Tests","volume":"33","author":"Diebold","year":"2015","journal-title":"J. Bus. Econ. Stat."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/1\/5\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:35:53Z","timestamp":1760196953000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/1\/5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,12,24]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["fi11010005"],"URL":"https:\/\/doi.org\/10.3390\/fi11010005","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,12,24]]}}}