EBITDA Index Prediction Using Exponential Smoothing and ARIMA 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.

Forecasting Selected Colombian Shares Using a Hybrid ARIMA-SVR Model

“…It is possible to adjust a variety of time series data using either ARs (autoregressive models), MAs (moving averages), or ARMA, which is a combination of AR and MA. Due to its effectiveness as a classical forecasting method, it has been widely used, for example, to predict water quality, workloads in cloud applications, the EBITDA index for financial performances, and short-term customer loads [13][14][15][16]. Since ARIMA is a linear model, it cannot capture non-linear patterns in a time series; therefore, different predictive models based on machine learning and deep learning techniques have been used in time series forecasting to predict strong fluctuations.…”

Análise de métodos híbridos para a previsão da demanda de energia elétrica baseados no ARIMA

“…O Departamento de Engenharia Nuclear da Universidade Federal de Minas Gerais (DEN/UFMG) já realizou estudos anteriores quanto ao erro percentual de três diferentes métodos em relação ao seu erro percentual, concluindo que o modelo de médias móveis integradas autorregressivas (ARIMA) apresentou os piores resultados [5]. Porém, o modelo de ARIMA ainda é um dos mais utilizados para a análise e previsão de séries temporais [6][7][8][9][10][11][12], e por isto, novas pesquisas foram realizadas numa tentativa de melhorar sua precisão, através de diferentes métodos de pré-processamento dos dados.…”

Comparative Analysis of Three Predictive Models of Performance Indicators with Results-Based Management: Cancer Data Statistics in a National Institute of Health

“…The ARIMA model was selected for this study due to its ability to predict results with autoregressive (AR) and moving average (MA) components when modeling changes in performance indicators over time. The ARIMA integration (I) component is added to determine the level of differentiation required to ensure data stationarity [ 12 ] and is effective in capturing linear patterns in time series data [ 13 ].…”