Pattern identification in prices of products and services of Telco/CSPs companies

• Autor(a) principal: Amorim, Miguel Ângelo Carvalho
• 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/10773/38715
• topic: Data science; Time series; Clustering; T-SNE; K-means

Competition in the commercial market is increasingly fierce. In general, companies want to be able to predict the behavior of the market, in order to adapt the price of their products so that they can remain active and competitive in this market. It is well-known that the market is volatile and constantly changing, so every day there may be a change in the value of products. This is what makes the theme of this dissertation relevant, its goal is to find patterns of interest in the market, making it possible to provide valuable information to the company that will help decision making related to the prices of its products. This work was developed in partnership with the company Ritain.io, under the project zenPrice™ - Competitive intelligence SaaS, based on time series with the evolution of the price of a given product during a certain period of time, where the daily price is read and stored in an information repository. The identification of patterns in the market can be done through clustering procedures that allow finding groups of similar products taking into account the characteristics of the time series. Along this dissertation, features (or measures) are explored in the time, spectral and statistical domains. Furthermore, there is an analysis of the impact on the clustering process when different features are chosen. Since this work makes use of a high volume of data, it is convenient to consider a reduction in the dimensionality of the data before performing the clustering, in order to reduce the complexity of the problem and the need for computational resources. Therefore, this dissertation will focus on nonlinear methods for dimensionality reduction, more specifically the t-SNE, where the influence of different parameters is also studied. Using this method and the k-means clustering algorithm, 5 clusters were formed, where 3 of them are non-separable and 2 clusters are very cohesive and separable from the remaining 3.