英文标题：
《Decomposition of Time Series Data of Stock Markets and its Implications
for Prediction: An Application for the Indian Auto Sector》
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作者：
Jaydip Sen and Tamal Datta Chaudhuri
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最新提交年份：
2016
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英文摘要：
With the rapid development and evolution of sophisticated algorithms for statistical analysis of time series data, the research community has started spending considerable effort in technical analysis of such data. Forecasting is also an area which has witnessed a paradigm shift in its approach. In this work, we have used the time series of the index values of the Auto sector in India during January 2010 to December 2015 for a deeper understanding of the behavior of its three constituent components, e.g., the Trend, the Seasonal component, and the Random component. Based on this structural analysis, we have also designed three approaches for forecasting and also computed their accuracy in prediction using suitably chosen training and test data sets. The results clearly demonstrate the accuracy of our decomposition results and efficiency of our forecasting techniques, even in presence of a dominant Random component in the time series.
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中文摘要：
随着时间序列数据统计分析复杂算法的快速发展和演变，研究界开始在此类数据的技术分析方面投入大量精力。预测也是一个方法范式转变的领域。在这项工作中，我们使用了2010年1月至2015年12月期间印度汽车行业指数值的时间序列，以深入了解其三个组成部分的行为，例如趋势、季节性成分和随机成分。基于这种结构分析，我们还设计了三种预测方法，并使用适当选择的训练和测试数据集计算了它们的预测精度。结果清楚地证明了我们的分解结果的准确性和预测技术的效率，即使在时间序列中存在主要的随机成分。
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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：Computer Science 计算机科学
二级分类：Other Computer Science 其他计算机科学
分类描述：This is the classification to use for documents that do not fit anywhere else.
这是用于不适合其他任何地方的文档的分类。
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