论文摘要
Autoregressive Integrated Moving Average, or ARIMA, is one example of method that widely used to forecast price or stock exchange in the form of univariate time-series data. Although ARIMA could handle data with trend, it does not support time series analysis for seasonal goods like apple. SARIMA needed for time series analysis, which is seasonally changed. Apple price prediction using SARIMA could help to monitor the stock safety level of apple, which is rotten quickly. Dataset of average apple data in Indonesia captured from PIHPS（Pusat Informasi Harga Pangan Strategis Nasional）, published by Bank of Indonesia in collaboration with Gamatechno. The objective of this research is to generate predictive stock information about apple’s price habit by means of the time series analysis. Data was taken until 109 month from year 2018. The best Sarima model for Indonesian market is SARIMA（1,0,0）x（0,0,0,12） with AIC（Akaike Information Criterion） point about-126,89658390969188. Although best model is selected, MAPE indicator show that the error was 99.47, which show that model is not good enough for predict the apple price only using univariate analysis.
论文目录
文章来源
类型: 国际会议
作者: Yosua Alvin Adi Soetrisno,Eko Handoyo,Muhammad Haikal Ilyasa,Denis,Enda Wista Sinuraya
来源: 2019 International Conference on Advanced Information Science and System (AISS 2019) 2019-11-15
年度: 2019
分类: 基础科学,经济与管理科学
专业: 数学,农业经济,市场研究与信息
单位: Electrical Engineering Diponegoro University
分类号: F313.7;O211.61
DOI: 10.26914/c.cnkihy.2019.078763
页码: 112-117
总页数: 6
文件大小: 327k