Abstract
Forecasting has become essential in different economic sectors for decision making in local and regional policies. Therefore, the aim of this paper is to use and compare performance of two linear models to predict future values of a measure of real profit for a group of companies in the fashion sector, as a financial strategy to determine the economic behavior of this industry. With forecasting purposes, Exponential Smoothing (ES) and autoregressive integrated moving averages (ARIMA) models were used for yearly data. ES and ARIMA models are widely used in statistical methods for time series forecasting. Accuracy metrics were used to select the model with best performance and ES parameters. For the real profit measure of the financial performance of the fashion sector in Colombia EBITDA (Earnings Before Interest, Taxes, Depreciation, and Amortization) was used and was calculated using multiple SQL queries.
Highlights
The influence of prediction through information platforms has been one of the advantages of industry 4.0, which has facilitated the assurance of information for public and private organizations
Colombia is a country with a textile tradition, whose fashion industry has been affected by economic and social changes producing a significant decrease in its real profit
Since there were no statistical models available to predict the future behavior of the profit accumulation for this sector and its companies, two predictive models, exponential smoothing and autoregressive integrated moving averages (ARIMA) were presented in this paper
Summary
The influence of prediction through information platforms has been one of the advantages of industry 4.0, which has facilitated the assurance of information for public and private organizations. Associations and institutions have needed to promote the economic development of companies and their sectors by adopting models that allow the identification of financial performance patterns and the understanding of the future impact of decisions made over different time periods, for the formulation of policies and programs at regional and local levels [5,6,7,8,9] To this end, several works have been developed to project future economic behavior based on annual data records, which according to Bova and Klyviene (2020), Alkaraan (2020), Dania, Xing and Amer (2020), Chen and Chen (2019), Hernandez de Cos, Ramos and Jimeno (2018), among others, projection models have many limitations, due to the number of observations, conventional techniques or short time series, which do not allow predicting long-term behavior [9,10,11,12,13]. It is a powerful forecasting model that can be used as an alternative to the popular Box–Jenkins ARIMA family of methods
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
