传统SARIMA模型与机器学习模型在巴基斯坦CPI数据建模中的比较分析

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Computational Intelligence and Soft Computing Pub Date : 2023-11-07 DOI:10.1155/2023/3236617
Moiz Qureshi, Arsalan Khan, Muhammad Daniyal, Kassim Tawiah, Zahid Mehmood
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引用次数: 0

摘要

背景。在经济理论中,稳定的消费者价格指数(CPI)和与之相关的低通货膨胀率(IR)比不稳定的价格指数更受欢迎。CPI被认为是衡量一个国家IR的主要变量。这些指数是价格变动指数,在货币政策决策中具有重要意义。在本研究中,不同的传统和机器学习方法已被应用于建模和预测巴基斯坦的CPI。方法。巴基斯坦1960年至2021年的年度CPI数据使用季节性自回归移动平均(SARIMA)、神经网络自回归(NNAR)和多层感知器(MLP)模型建模。采用均方根误差(RMSE)、均方误差(MSE)和平均绝对百分比误差(MAPE)作为关键绩效指标(kpi),对几种形式的模型进行比较。结果。20隐层MLP模型是基于kpi预测CPI的最佳模型。从2022年到2031年,巴基斯坦的CPI预测值出现了天文数字般的增长,这让消费者和经济管理部门感到不快。结论。观察到的CPI上升趋势如果不加以解决,将引发购买力上升,从而导致商品价格上涨。建议政府制定有力的政策来应对这一令人担忧的局面。
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A Comparative Analysis of Traditional SARIMA and Machine Learning Models for CPI Data Modelling in Pakistan
Background. In economic theory, a steady consumer price index (CPI) and its associated low inflation rate (IR) are very much preferred to a volatile one. CPI is considered a major variable in measuring the IR of a country. These indices are those of price changes and have major significance in monetary policy decisions. In this study, different conventional and machine learning methodologies have been applied to model and forecast the CPI of Pakistan. Methods. Pakistan’s yearly CPI data from 1960 to 2021 were modelled using seasonal autoregressive moving average (SARIMA), neural network autoregressive (NNAR), and multilayer perceptron (MLP) models. Several forms of the models were compared by employing the root mean square error (RMSE), mean square error (MSE), and mean absolute percentage error (MAPE) as the key performance indicators (KPIs). Results. The 20-hidden-layered MLP model appeared as the best-performing model for CPI forecasting based on the KPIs. Forecasted values of Pakistan’s CPI from 2022 to 2031 showed an astronomical increase in value which is unpleasant to consumers and economic management. Conclusion. The increasing CPI trend observed if not addressed will trigger a rising purchasing power, thereby causing higher commodity prices. It is recommended that the government put vibrant policies in place to address this alarming situation.
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来源期刊
Applied Computational Intelligence and Soft Computing
Applied Computational Intelligence and Soft Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
6.10
自引率
3.40%
发文量
59
审稿时长
21 weeks
期刊介绍: Applied Computational Intelligence and Soft Computing will focus on the disciplines of computer science, engineering, and mathematics. The scope of the journal includes developing applications related to all aspects of natural and social sciences by employing the technologies of computational intelligence and soft computing. The new applications of using computational intelligence and soft computing are still in development. Although computational intelligence and soft computing are established fields, the new applications of using computational intelligence and soft computing can be regarded as an emerging field, which is the focus of this journal.
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