Decomposition of Time Series Data of Stock Markets and its Implications for Prediction: An Application for the Indian Auto Sector

TL;DR

This paper decomposes Indian auto sector stock index time series into trend, seasonal, and random components, and proposes forecasting methods that demonstrate high accuracy despite dominant randomness.

Contribution

It introduces a detailed decomposition approach for stock market time series and develops three forecasting methods validated on real Indian auto sector data.

Findings

Decomposition accurately identifies underlying components.

Forecasting methods achieve high prediction accuracy.

Effective even with dominant random components.

Abstract

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.