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

机译：利用跨域开放数据融合的大宗商品价格趋势预测

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摘要

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, (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.
机译：预测大宗商品的价格趋势在国际贸易中很重要，这不仅对于市场参与者制定生产和销售计划，而且对于政府管理人员调整政策也很重要。以前的研究不能支持准确的细粒度短期预测，因为它们主要关注使用历史数据进行的粗粒度长期预测。近来，跨域开放数据提供了进行细粒度价格预测的可能性，因为可以利用它们来提取各种直接和间接的价格因素。在本文中，我们将利用跨域开放数据融合来预测未来几天的价格趋势。更具体地说，我们将价格趋势划分为三个类别（上升，轻微变化和下降），然后预测未来一天价格趋势所在的特定类别。我们考虑以下三个因素：（1）供应因素考虑提供大宗商品的来源;（2）需求因素侧重于反映短期需求的船舶运输;（3）预期因素包括间接特征（例如，空气质量）潜在的影响。提出了一种用于价格趋势预测的混合分类框架。对9个现实世界中跨域开放数据集进行的评估表明，我们的框架可以准确地预测价格趋势，优于多个最新基准。

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