A Time Series Analysis-Based Forecasting Framework for the Indian Healthcare Sector

TL;DR

This paper presents a time series analysis framework for forecasting the Indian healthcare sector index, utilizing decomposition and six different predictive methods to improve accuracy and insights.

Contribution

It introduces a novel combination of decomposition and multiple forecasting methods specifically applied to the Indian healthcare sector time series data.

Findings

Decomposition reveals key behavioral insights of the healthcare index.

Six forecasting methods are evaluated for effectiveness.

Results demonstrate improved forecasting accuracy.

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.