Decomposition of Time Series Data of Stock Markets and its Implications for Prediction – An Application for the Indian Auto Sector
Proceedings of the 2nd National Conference on Advances in Business Research and Practices (ABRMP 2016), January 8-9, 2016, Kolkata, INDIA.
14 Pages Posted: 11 Apr 2016 Last revised: 27 Apr 2016
Date Written: January 8, 2016
With 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.
Keywords: Decomposition, Trend, Seasonal, Random, Holt Winters Forecasting model, Neural Network, Back Propagation Network, ARIMA, VAR, Bayesian Vector Autoregressive (BVAR) model
JEL Classification: G11, G14, G17, C63
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