2018
DOI: 10.3390/fi11010005
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Forecasting E-Commerce Products Prices by Combining an Autoregressive Integrated Moving Average (ARIMA) Model and Google Trends Data

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 to… Show more

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Cited by 51 publications
(33 citation statements)
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“…13. Dependence of effectiveness of the demand forecasting process on the use of the sales volume (1) and cointegrated series (2) as forecast series…”
Section: The Methods and Results Of Comparative Study Of Forecasting Effectivenessmentioning
confidence: 99%
See 1 more Smart Citation
“…13. Dependence of effectiveness of the demand forecasting process on the use of the sales volume (1) and cointegrated series (2) as forecast series…”
Section: The Methods and Results Of Comparative Study Of Forecasting Effectivenessmentioning
confidence: 99%
“…Level of demand for a particular consumer product is one of the main factors determining the feasibility of business development and its scope [1]. Since the demand for a consumer product is closely connected with its price characteristic [2], in conditions of a saturated market, economic structures are faced with the problem of bettering efficiency of production and distribution of final products. Effectiveness of the system process is influenced by many factors.…”
Section: Introductionmentioning
confidence: 99%
“…Specifically, the literature shows how the exploitation of information from social media becomes increasingly important. In [26], such information have been exploited in order to predict e-commerce products prices, whereas, in [27], the authors performed an evaluation of products and services through unsolicited social contents.…”
Section: Popularity In Social Mediamentioning
confidence: 99%
“…The work in [33] used standardized technical indicators to forecast rise or fall of market prices with the AdaBoost algorithm, which is used to optimize the weight of these technical indicators. In [34,35], the authors used an Auto Regressive Integrated Moving Average (ARIMA) in pre-processed time series data in order to predict prices. The authors in [36] proposed a hybrid approach, based on Deep Recurrent Neural Networks and ARIMA in a two-step forecasting technique to predict and smooth the predicted prices.…”
Section: Background and Related Workmentioning
confidence: 99%