research-article

Forecasting Price Trend of Bulk Commodities Leveraging Cross-domain Open Data Fusion

Authors:

Gang PanAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 11, Issue 1

Article No.: 1, Pages 1 - 26

https://doi.org/10.1145/3354287

Published: 21 January 2020 Publication History

Abstract

Forecasting price trend of bulk commodities is important in international trade, not only for markets participants to schedule production and marketing plans but also for government administrators to adjust policies. Previous studies cannot support accurate fine-grained short-term prediction, since they mainly focus on coarse-grained long-term prediction using historical data. Recently, cross-domain open data provides possibilities to conduct fine-grained price forecasting, since they can be leveraged to extract various direct and indirect factors of the price. In this article, we predict the price trend over upcoming days, by leveraging cross-domain open data fusion. More specifically, we formulate the price trend into three classes (rise, slight-change, and fall), and then we predict the specific class in which the price trend of the future day lies. We take three factors into consideration: (1) supply factor considering sources providing bulk commodities,<?brk?> (2) demand factor focusing on vessel transportation with reflection of short time needs, and (3) expectation factor encompassing indirect features (e.g., air quality) with latent influences. A hybrid classification framework is proposed for the price trend forecasting. Evaluation conducted on nine real-world cross-domain open datasets shows that our framework can forecast the price trend accurately, outperforming multiple state-of-the-art baselines.

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Cited By

Falardeau JHadzagic MShahbazian E(2023)Cloud-Based Knowledge Extraction (CBEx) System: A Solution for Real-time Data Analysis for Rapid Decision Making2023 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)10.1109/CCECE58730.2023.10289046(324-330)Online publication date: 24-Sep-2023
https://doi.org/10.1109/CCECE58730.2023.10289046
Zhou BChen LZhao SZhou FLi SPan G(2023)Spatio-temporal analysis of urban crime leveraging multisource crowdsensed dataPersonal and Ubiquitous Computing10.1007/s00779-020-01456-627:3(599-612)Online publication date: 1-Jun-2023
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Kengpol ATabkosai P(2023)Hybrid deep learning cost evaluation using CNN with ANN for the plastic injection industryNeural Computing and Applications10.1007/s00521-023-08947-635:31(23153-23175)Online publication date: 1-Nov-2023
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Index Terms

Forecasting Price Trend of Bulk Commodities Leveraging Cross-domain Open Data Fusion
1. Applied computing
  1. Law, social and behavioral sciences
    1. Economics
  2. Operations research
    1. Forecasting
    2. Marketing
2. Information systems
  1. Information systems applications
    1. Data mining

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 11, Issue 1

February 2020

304 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3375625

Editor:
Yu Zheng
JD Finance, China

Issue’s Table of Contents

Copyright © 2020 ACM.

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Publication History

Published: 21 January 2020

Accepted: 01 August 2019

Revised: 01 July 2019

Received: 01 May 2019

Published in TIST Volume 11, Issue 1

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Cited By

Falardeau JHadzagic MShahbazian E(2023)Cloud-Based Knowledge Extraction (CBEx) System: A Solution for Real-time Data Analysis for Rapid Decision Making2023 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)10.1109/CCECE58730.2023.10289046(324-330)Online publication date: 24-Sep-2023
https://doi.org/10.1109/CCECE58730.2023.10289046
Zhou BChen LZhao SZhou FLi SPan G(2023)Spatio-temporal analysis of urban crime leveraging multisource crowdsensed dataPersonal and Ubiquitous Computing10.1007/s00779-020-01456-627:3(599-612)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.1007/s00779-020-01456-6
Kengpol ATabkosai P(2023)Hybrid deep learning cost evaluation using CNN with ANN for the plastic injection industryNeural Computing and Applications10.1007/s00521-023-08947-635:31(23153-23175)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s00521-023-08947-6
An WWang LZeng Y(2022)Text‐based soybean futures price forecasting: A two‐stage deep learning approachJournal of Forecasting10.1002/for.290942:2(312-330)Online publication date: 13-Sep-2022
https://doi.org/10.1002/for.2909
Santos GBarboza FVeiga ASilva M(2021)Forecasting Brazilian Ethanol Spot Prices Using LSTMEnergies10.3390/en1423798714:23(7987)Online publication date: 30-Nov-2021
https://doi.org/10.3390/en14237987
Zhou BChen LZhou FLi SZhao SDas SPan G(2021)ESCORT: Fine-Grained Urban Crime Risk Inference Leveraging Heterogeneous Open DataIEEE Systems Journal10.1109/JSYST.2020.302376215:3(4656-4667)Online publication date: Sep-2021
https://doi.org/10.1109/JSYST.2020.3023762

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