Arima Model of Outlier Detection for Forecasting Consumer Price Index (Cpi)

Imron, M.; Utami, Wika Dianita; Khaulasari, Hani; Armunanto, Firman

doi:10.30598/barekengvol16iss4pp1259-1270

Cited by 8 publications

(8 citation statements)

References 20 publications

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“…Notably, the investigation identified ARIMA(1,2,8) as yielding the most favorable outcomes among the considered ARIMA models. This finding aligns with the broader research landscape, where scholars, such as Ibrahim et al (2022) in the context of Nigerian Consumer Price Index (CPI) forecasting and Imron et al (2022) for Probolinggo City's CPI, have similarly engaged ARIMA models, albeit with distinct parameter specifications tailored to the specific economic characteristics of their respective regions. The diversity in optimal ARIMA model across countries underscores the unique nature of economic changes and necessitates the development of context-specific models.…”

Section: Discussionsupporting

confidence: 61%

ARIMA Modeling and Forecasting of National Consumer Price Index in Nepal

Poudel,

Kharel,

Acharya

et al. 2024

Interdisc. J. Mgt. Soc. Sci.

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This research aims to determine an Auto-Regressive Integrated Moving Average (ARIMA) model and forecasting the National Consumer Price Index (NCPI) in Nepal, using annual data from the fiscal year 1972/73 to 2022/23. The research utilized secondary data collected from the online bulletin published by Nepal Rastra Bank, aiming to enhance understanding of NCPI patterns and contribute to predictive modelling techniques for economic indicators. Adopting the Box-Jenkins technique and using E-Views statistical software, this study identifies ARIMA (1, 2, 8) as the most suitable model for NCPI forecasting. This selection is validated through meticulous diagnostic tests, affirming the appropriateness of the model, as evidenced by residuals exhibiting white noise characteristics. The research findings suggest a rapid increase in the National Consumer Price Index for the coming years. Implications of this research extend to both academic and policy-oriented audiences. A successful ARIMA model development holds significant potential for providing accurate predictions of future price trends in Nepal. This, in turn, can assist policymakers in making informed decisions regarding economic strategies, monetary policies, and inflation control measures. Moreover, the study's outcomes offer a foundational framework for future research endeavours in the domain of economic forecasting and modelling, contributing to the broader field of economic analysis and policy formulation.

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Section: Discussionsupporting

confidence: 61%

ARIMA Modeling and Forecasting of National Consumer Price Index in Nepal

Poudel,

Kharel,

Acharya

et al. 2024

Interdisc. J. Mgt. Soc. Sci.

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“…Therefore, ARIMA model is only suitable for short-term forecasting. If the forecasting time is too long, it will increase the forecasting error and affect the forecasting accuracy [73,74]. With rapid economic development and urbanization, the intensity of land development is increasing, leading to the deterioration of the ecological environment.…”

Section: Discussionmentioning

confidence: 99%

Analysis and Prediction of Land Resources’ Carrying Capacity in 31 Provinces of China from 2008 to 2016

Han

Zheng

2021

Sustainability

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The rapid development of urbanization, population growth, and unreasonable use of land resources have made the contradiction between human beings and land increasingly prominent, and the carrying capacity of land resources has become an important factor affecting the sustainable development of a city or even a country. Based on the carrying capacity of agricultural land, construction land, and ecological land in 31 provinces of China, this paper analyzed the spatiotemporal characteristics of the carrying capacity of land resources in 31 provinces of China from 2008 to 2016 by using single-factor carrying capacity evaluation and comprehensive carrying capacity evaluation methods. The evaluation results were divided into six levels (No pressure, Lower pressure, Low pressure, Medium pressure, High pressure, Higher pressure), and the ArcGIS10.2 software was used for visualization, and the carrying capacity prediction model was constructed to predict the development trend of the comprehensive carrying capacity of land resources in 2020 and 2025. The results showed that China’s land carrying capacity is extremely unbalanced. Whether from the evaluation results of single-factor carrying capacity or comprehensive carrying capacity, we showed the characteristics of high pressure in the east and low pressure in the west, and that the regional land resources have a deteriorating trend. Through the prediction of the future land resource carrying capacity index, it was found that the comprehensive carrying capacity index of land resources in 31 provinces of China will still show an upward trend in the future.

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“…i.e. (4) Where 𝜖 𝑡 denotes a white noise sequence. Specially, when 𝑋 𝑡 equals to 𝜇 𝑡 , which means the current values of the time series unrealted to the historical values, but depend only on a linear combination of historical white noise: (5) Highlights in Business, Economics and Management…”

Section: Ma (Moving Average) Modelmentioning

confidence: 99%

Forecasting Analysis of the Hungarian CPI Based on the Arima Model

2024

HBEM

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Since the twenty-first century, under the influence of economic system reform and economic globalization, Hungary's economic development has prospered, and the GDP index has increased significantly, however, while the country's economic development level has increased year by year, the consumer price index has also shown a year by year increasing trend. Consumer price index (CPI) is closely related to the quality of life, employment and economic stability. In this paper, the CPI in 2021 is taken as the base (100), and the data from 1990 to 2021 are statistically analyzed, and the ARIMA model is used to make time-series forecasts. Based on the ADF test, autocorrelation and partial autocorrelation plots for the determination of the series parameters, the ARIMA (1, 1, 0) model is obtained, and the final results are obtained for the ten data forecasts after 2021. The results show that the Hungarian consumer price index will still grow in the next ten years, but unlike the exponential growth after 2017, the growth rate slows down in the next ten years, indicating that the economic situation will stabilize in the future and price fluctuations will not be too high.

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Arima Model of Outlier Detection for Forecasting Consumer Price Index (Cpi)

Cited by 8 publications

References 20 publications

ARIMA Modeling and Forecasting of National Consumer Price Index in Nepal

ARIMA Modeling and Forecasting of National Consumer Price Index in Nepal

Analysis and Prediction of Land Resources’ Carrying Capacity in 31 Provinces of China from 2008 to 2016

Forecasting Analysis of the Hungarian CPI Based on the Arima Model

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