Paper Info
Title
Forecasting Price Trend of Bulk Commodities Leveraging Cross-domain Open Data Fusion.
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.
Year
DOI
Venue
2020
10.1145/3354287
ACM Transactions on Intelligent Systems and Technology
Keywords
DocType
Volume
Price trend,bulk commodity,cross-domain data,data fusion,multi-class prediction
Journal
11
Issue
ISSN
Citations 
1
2157-6904
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Binbin Zhou102.03
Sha Zhao2489.96
Longbiao Chen312310.60
Shijian Li4115569.34
Zhaohui Wu53121246.32
Gang Pan61501123.57