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Cited by 59Forecasting Techniques and ApplicationsStock Market Forecasting MethodsTime Series Analysis and Forecasting

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

A hybrid probabilistic fuzzy ARIMA model for consumption forecasting in commodity markets

Torbat, Sheida, Khashei, Mehdi, Bijari, Mehdi·2018·Economic Analysis and Policy
DOI 10.1016/j.eap.2017.12.003
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Abstract

Consumption forecasting is a critical issue in commodity markets on which financial decision-makers depend for accuracy. To adequately handle the complexity and uncertainty associated with real-world market problems, forecasting needs to be capable of handling complex situations. The steel industry is a strategic one for Iran playing a critical role in the national economy. Using time series models, this study aims to forecast the future trend of Iran’s crude steel consumption. Although autoregressive integrated moving average (ARIMA) models are regarded as the most important time series models and are extensively employed in forecasting financial markets, they are hampered by certain limitations that detract from their popularity. They are based on the assumption that a linear relationship holds between future values of a time series and its current and past values. Moreover, they depend heavily on a large amount of historical data to provide the desired results. To overcome the limitations in such conventional models, fuzzy autoregressive integrated moving average models have been proposed as improved versions of the ARIMA models. Unfortunately, the former are also plagued by very wide forecasted intervals in cases where there are outliers that create instability in the data. The present paper proposes a hybrid model which is a combination of computational intelligence tools and soft computing techniques. In such a form they take advantage of their unique properties which, when exploited, can provide more accurate financial forecasts. The main objective of the proposed model is to identify nonlinear patterns with probabilistic classifiers to obtain narrower intervals than would be otherwise possible under the traditional FARIMA models. The empirical results obtained from applying the proposed model to forecasting Iran’s steel consumption provide significantly improved accuracy.

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Crossref · 10.1016/j.eap.2017.12.003

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Citations

Cites

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Research works printed but not linked: 1

  1. Ishibuchi, 1988, Interval regression analysis based on mixed 0–1 integer programming problem, J. Japan Soc. Ind. Eng, 40, 312

Cited by

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Metadata

Authors
Torbat, S., Khashei, M., & Bijari, M.
Affiliations
Isfahan University of Technology (IR)
Corresponding author
Khashei, Mehdi
ORCID
Khashei, Mehdi
Venue
Economic Analysis and Policy
Publisher
Elsevier BV
Published
2018-06
Volume
58
Pages
22-31
ISSN
0313-5926 (ISSN-L)
DOI
10.1016/j.eap.2017.12.003
OpenAlex
W2782613816
Cited by
56
References
31
Citations by year
2026: 3 · 2025: 5 · 2024: 5 · 2023: 12 · 2022: 10 · 2021: 8 · 2020: 4 · 2019: 6 · 2018: 3
FWCI
4.19
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Economic Analysis and Policy (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
  • Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
Keywords
consumption forecasting, commodity markets, hybrid forecasting model, probabilistic classifiers, outliers, steel industry, Iran

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.