摘要翻译：
在过去的二十年里，人们提出了许多基于模糊时间序列的预测方法。汽车交通事故的预测大多采用模糊时间序列方法。然而，现有方法的预测准确率都不够高。本文将本文提出的模糊时间序列预测新方法与现有方法进行了比较。我们的方法是基于均值的道路交通事故历史数据分割。该方法属于K阶时变方法。该方法对汽车交通事故的预测准确率比已有的方法要高。
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英文标题：
《Inaccuracy Minimization by Partioning Fuzzy Data Sets - Validation of
  Analystical Methodology》
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作者：
G. Arutchelvan, S. K. Srivatsa, R. Jagannathan
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最新提交年份：
2010
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  In the last two decades, a number of methods have been proposed for forecasting based on fuzzy time series. Most of the fuzzy time series methods are presented for forecasting of car road accidents. However, the forecasting accuracy rates of the existing methods are not good enough. In this paper, we compared our proposed new method of fuzzy time series forecasting with existing methods. Our method is based on means based partitioning of the historical data of car road accidents. The proposed method belongs to the kth order and time-variant methods. The proposed method can get the best forecasting accuracy rate for forecasting the car road accidents than the existing methods.
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PDF链接：
https://arxiv.org/pdf/1005.4272