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One of the challenging research problems in the domain of time seriesanalysis and forecasting is making efficient and robust prediction of stockmarket prices. With rapid development and evolution of sophisticated algorithmsand with the availability of extremely fast computing platforms, it has nowbecome possible to effectively extract, store, process and analyze high volumestock market time series data. Complex algorithms for forecasting are nowavailable for speedy execution over parallel architecture leading to fairlyaccurate results. In this paper, we have used time series data of the twosectors of the Indian economy: Consumer Durables sector and the Small Capsector for the period January 2010 to December 2015 and proposed adecomposition approach for better understanding of the behavior of each of thetime series. Our contention is that various sectors reveal different timeseries patterns and understanding them is essential for portfolio formation.Further, based on this structural analysis, we have also proposed severalrobust forecasting techniques and analyzed their accuracy in prediction usingsuitably chosen training and test data sets. Extensive results are presented todemonstrate the effectiveness of our propositions.

An Alternative Framework for Time Series Decomposition and Forecasting  and its Relevance for Portfolio Choice: A Comparative Study of the Indian  Consumer Durable and Small Cap Sectors