On Holt Winters Algorithm with Decomposition for Forecasting Financial Time Series with Complex Seasonal Patterns

Abstract

One of the most important challenges when analyzing and forecasting the time series is the stability of the series and determining components of the time series such as trend and seasonal. Exponential Smoothing methods can be thought of as peers and alternatives to Box-Jenkins ARIMA class of time series forecasting methods, but the most important aspect of the exponential smoothing approach is that the time series does not have to be stable. The study introduces reviewing and comparing a variety of Exponential Smoothing models; Simple Exponential Smoothing (SES), Holt’s Linear Exponential Smoothing or Double Exponential Smoothing (DES) and Holt Winters Algorithm or Triple Exponential Smoothing (TES). Additionally; creation temporal patterns to forecast the monthly stock returns of the Saudi Stock Index by using a variety of Exponential Smoothing models. The results of the study concluded that the Holt Winters Algorithm or triple exponential smoothing model is the best model since it produces the lowest Mean Absolute Percentage error (MAPE), Mean Absolute Deviation (MAD), and Mean Squared Deviation (MSD) values which are 4,380, 244783 compared to 4, 385, 246837 for SES, and 5, 410, 270734 for DES, and thus can be used to predict the monthly stock returns of the Saudi Stock Index.

Keywords

• single exponential smoothing
• holt’s linear exponential smoothing
• holt winters algorithm
• moving average
• financial time series analysis