A Time Series Analysis-Based Forecasting Framework for the Indian
Healthcare Sector
- Jaydip Sen ,
- Tamal Datta Chaudhuri
Abstract
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.