Time series analysis for price recommendation in the telecommunications market

• Autor(a) principal: Cruz, Vitória Isabel Escudeiro
• Data de Publicação: 2022
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10773/39405
• topic: Data science; Time series; Forecasting; ARIMA; Prophet; Prices

zenPrice™ is a SaaS solution created by the company Ritain.io that collects, via web-scrapping, the prices of various products in the e-commerce market and then makes them available through a centralised platform to its customers, which are usually companies that also sell this type of products. The platform can be improved through the introduction of new algorithms and methods capable of better capturing patterns and important information in the data. After meetings with Ritain.io, three functionalities capable of producing relevant insights were identified: multi-day price forecast, one-day price change forecast and competitor profile identification. The objective of this work is the implementation and study of techniques and statistical models that can later serve as a basis for the development of those functionalities. To carry out the multi-day price forecast, the ARIMA and Prophet models from Facebook were used, the latter having achieved the desired result when used in a multivariate approach, which led to the conclusion that using only the prices of the previous days of a product to predict the future prices of that same product is insufficient. Predicting a price change is a much simpler problem than predicting prices and, as this is a discrete and not continuous problem, different models have been used, such as Markov Chains and LSTM. Finally, to identify a competitor’s profile, which, in a simplified way, involves identifying time-varying lag and lead relationships between time series, an analysis of the coefficients of linear regression models was performed. Other approaches, such as Dynamic Time Warping and cross-correlation, were also discussed. An overview of the research work and the Python code used to generate the results presented in this dissertation are available at Github (github.com/ieeta-pt/zenPriceTSA).