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.

本文通过对 2010 年 1 月至 2016 年 12 月印度医疗保健部门的时间序列数据进行分解，并基于对其结构的分析提出了六种不同的预测方法，进而证明了这些方法的有效性。