英文标题：
《A Time Series Analysis-Based Forecasting Framework for the Indian
  Healthcare Sector》
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作者：
Jaydip Sen and Tamal Datta Chaudhuri
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最新提交年份：
2017
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英文摘要：
  Designing efficient and robust algorithms for accurate prediction of stock market prices is one of the most exciting challenges in the field of time series analysis and forecasting. With the exponential rate of development and evolution of sophisticated algorithms and with the availability of fast computing platforms, it has now become possible to effectively and efficiently extract, store, process and analyze high volume of stock market data with diversity in its contents. Availability of complex algorithms which can execute very fast on parallel architecture over the cloud has made it possible to achieve higher accuracy in forecasting results while reducing the time required for computation. In this paper, we use the time series data of the healthcare sector of India for the period January 2010 till December 2016. We first demonstrate a decomposition approach of the time series and then illustrate how the decomposition results provide us with useful insights into the behavior and properties exhibited by the time series. Further, based on the structural analysis of the time series, we propose six different methods of forecasting for predicting the time series index of the healthcare sector. Extensive results are provided on the performance of the forecasting methods to demonstrate their effectiveness.
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中文摘要：
在时间序列分析和预测领域，设计高效、稳健的算法来准确预测股票市场价格是最令人兴奋的挑战之一。随着复杂算法的指数级发展和演变，以及快速计算平台的可用性，现在可以有效地提取、存储、处理和分析大量内容多样的股票市场数据。复杂算法的可用性可以在云上的并行架构上快速执行，这使得预测结果的准确性更高，同时减少了计算所需的时间。在本文中，我们使用了2010年1月至2016年12月期间印度医疗行业的时间序列数据。我们首先演示时间序列的分解方法，然后说明分解结果如何为我们提供关于时间序列所显示的行为和属性的有用见解。此外，基于时间序列的结构分析，我们提出了六种不同的预测方法来预测医疗行业的时间序列指数。对预测方法的性能提供了广泛的结果，以证明其有效性。
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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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