摘要翻译：
本研究涉及分析和绘制瑞士法郎利率。利率取决于时间和期限，定义了利率曲线(IRC)的期限结构。在本研究中，IRC被认为是一个二维特征，时空和成熟度。应用地质统计模型和机器学习算法（多层感知器和支持向量机）生成利率地图。IR地图可用于可视化和模式感知目的，开发和探索经济假设，产生动态评估负债模拟和财务风险评估。本文还讨论了利率映射法在IRC预测中应用的可行性。
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英文标题：
《Interest rates mapping》
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作者：
M. Kanevski, M. Maignan, A. Pozdnoukhov, V. Timonin
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最新提交年份：
2007
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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一级分类：Physics        物理学
二级分类：Data Analysis, Statistics and Probability        数据分析、统计与概率
分类描述：Methods, software and hardware for physics data analysis: data processing and storage; measurement methodology; statistical and mathematical aspects such as parametrization and uncertainties.
物理数据分析的方法、软硬件：数据处理与存储；测量方法；统计和数学方面，如参数化和不确定性。
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英文摘要：
  The present study deals with the analysis and mapping of Swiss franc interest rates. Interest rates depend on time and maturity, defining term structure of the interest rate curves (IRC). In the present study IRC are considered in a two-dimensional feature space - time and maturity. Geostatistical models and machine learning algorithms (multilayer perceptron and Support Vector Machines) were applied to produce interest rate maps. IR maps can be used for the visualisation and patterns perception purposes, to develop and to explore economical hypotheses, to produce dynamic asses-liability simulations and for the financial risk assessments. The feasibility of an application of interest rates mapping approach for the IRC forecasting is considered as well.
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PDF链接：
https://arxiv.org/pdf/0709.4361