Constructing a Sales Forscasting Method in Fashion Retail

Student: Panov Fedor

Supervisor: Maria Veretennikova

Educational Programme: Statistical Modelling and Actuarial Science (Master)

Year of Graduation: 2019

Forecasting of future sales is an important tool for managing any retailer. Forecasting is particularly important in the fashion retail. Fashion retail sales are characterized by high volatility and a very short life cycle (from six month to a month). In this article, a new hybrid forecasting method was designed based on SARIMA methods and the extended extreme learning machine (EELM) that is a learning technique for feed-forward one-hidden layer artificial neural networks. The detailed scheme for an adaptation of the hybrid method to do forecasting for real sales data from large fashion retailer was shown. This scheme can be used to adapt other methods based on SARIMA and EELM. In the end, a comparison was made between performance of the SARIMA-EELM, SARIMA and EELM models using various metrics.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses