<sans-serif>EBITDA</sans-serif> Index Prediction Using <sans-serif>Exponential Smoothing</sans-serif> and <sans-serif>ARIMA</sans-serif> Model

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.

Keywords

• time series forecasting
• exponential smoothing models
• decision-making
• <sans-serif>ARIMA</sans-serif>
• <sans-serif>EBITDA</sans-serif>
• fashion industry sector