  Analysis and prediction of stock market time series data has attracted
considerable interest from the research community over the last decade. Rapid
development and evolution of sophisticated algorithms for statistical analysis
of time series data, and availability of high-performance hardware has made it
possible to process and analyze high volume stock market time series data
effectively, in real-time. Among many other important characteristics and
behavior of such data, forecasting is an area which has witnessed considerable
focus. In this work, we have used 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 five approaches for forecasting and also
computed their accuracy in prediction using suitably chosen training and test
data sets. Extensive results are presented to demonstrate the effectiveness of
our proposed decomposition approaches of time series and the efficiency of our
forecasting techniques, even in presence of a random component and a sharply
changing trend component in the time-series.
