LQ45 Stock Price Forecasting: A Comparison Study of Arima(p,d,q) and Holt-Winter Method

Abstract

The Holt-Winter method and ARIMA(p,d,q) are two frequently used forecasting techniques. When using ARIMA, errors are expected to be connected with earlier errors because it is based on data correlation with prior data (autoregressive) (moving average). The Holt-Winter model comes in two different forms: Multiplicative Holt-Winter and Additive Holt-Winter. No one has ever attempted to compare combined time series and cross-section data, despite the fact that there has been a great deal of prior study on ARIMA and Holt-Winter. In a combined time-series and cross-section dataset, the accuracy rates of Holt-Winter and ARIMA(p,d,q) will be compared in this study. LQ45 stock prices are used because they track the performance of 45 stocks with substantial liquidity, sizable market caps, and solid underlying businesses. The Mean Absolute Percentage Error (MAPE) method is used to gauge accuracy. This study contributes to MAPE exploration by using a Boxplot diagram from cross-sectional data. With the Boxplot diagram, we can see the MAPE spread, the MAPE's center point, and the presence of outliers from the MAPE of LQ45 stock. According to the findings of this empirical study, the average error rate for predicting LQ45 stock prices using ARIMA is 7,0390%, with a standard deviation of 7,7441%; for multiplying Holt-Winter, it is 29,3919%, with a standard deviation of 25,7571%; and for additive Holt-Winter, it is 18,0463%, with a standard deviation of 18,3504%. Apart from numerical comparisons, it can also be seen visually, based on the Boxplot diagram, that the MAPE of ARIMA(p,d,q) is more focused than Holt-Winter. In addition, in terms of accuracy distribution, it can be seen that the MAPE accuracy of the ARIMA method produces four outliers. Based on the MAPE accuracy rate, we conclude that Holt-Winter has a bigger error based on the MAPE value than ARIMA(p,d,q) at forecasting LQ45 stock prices.