The Inflation Forecasting of Major Cities In East Kalimantan: A Comparison Of Holt-Winters And SARIMA Model

Regi Muzio Ponziani
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Abstract

This research aims to compare the performance of Holt Winters and Seasonal Autoregressive Integrate Moving Average (SARIMA) models in predicting inflation in Balikpapan and Samarinda, two biggest cities in East Kalimantan province. The importance of East Kalimantan province cannot be overstated since it has been declared as the venue for the capital of Indonesia. Hence, inflation prediction of the two cities will give valuable insights about the economic nature of the province for the country’s new capital. The data used in this study extended from January 2015 to September 2021. The data were divided into training and test data. The training data were used to model the time series equation using Holt winters and SARIMA models. Later, the models derived from training data were employed to produce forecasts. The forecasts were compared to the actual inflation data to determine the appropriate model for forecasting. Test data were from January 2015 to December 2020 and test data extended from January 2021 to September 2021. The result showed that Holt-Winters performed better than SARIMA in prediction inflation. The Root Mean Squared Error (RMSE) values are lower for Holt-Winters Exponential Smoothing for both cities. It also predicts better timing of cyclicality than SARIMA model.
东加里曼丹主要城市通货膨胀预测:基于Holt-Winters和SARIMA模型的比较
本研究旨在比较Holt Winters和季节性自回归综合移动平均(SARIMA)模型在预测东加里曼丹省两个最大城市巴里巴潘和萨马林达的通货膨胀方面的表现。东加里曼丹省的重要性怎么强调都不为过,因为它已被宣布为印度尼西亚的首都所在地。因此,对这两个城市的通胀预测将为这个国家的新首都提供有关该省经济性质的宝贵见解。本研究使用的数据从2015年1月延长到2021年9月。数据分为训练数据和测试数据。训练数据使用Holt winters和SARIMA模型对时间序列方程进行建模。随后,利用训练数据得到的模型进行预测。将预测结果与实际通胀数据进行比较,以确定合适的预测模型。测试数据为2015年1月至2020年12月,测试数据为2021年1月至2021年9月。结果表明,Holt-Winters在预测通货膨胀方面优于SARIMA。两个城市的霍尔特温特斯指数平滑的均方根误差(RMSE)值更低。它还能比SARIMA模型更好地预测周期性的时间。
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