Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
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
Open this paper in CoChat — free account

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.

Overview

Integrity signals

Registry

Crossref · 10.1016/j.eap.2017.12.003

Cites

33 of 33 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Citations

Cites

Show all 31 references

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

See all 59

Related

See all

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: A hybrid probabilistic fuzzy ARIMA model for consumption forecasting in commodity markets. https://app.cochat.ai/papers/10.1016/j.eap.2017.12.003
The paper
Torbat, S., Khashei, M., & Bijari, M. (2018). A hybrid probabilistic fuzzy ARIMA model for consumption forecasting in commodity markets. Economic Analysis and Policy. https://doi.org/10.1016/j.eap.2017.12.003

Data sources

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.