A Time Series Analysis - Based Forecasting Approach for the Indian Realty Sector
International Journal of Applied Economic Studies, Vol. 5, Issue 4, August 2017
20 Pages Posted: 24 Jul 2017
Date Written: July 20, 2017
Prediction of stock prices using time series analysis is quite a difficult and challenging task since the stock prices usually depict random patterns of movement. However, the last decade has witnessed rapid development and evolution of sophisticated algorithms for complex statistical analysis. These algorithms are capable of processing a large volume of time series data executing on high-performance hardware and parallel computing architecture. Thus computations which were seemingly impossible to perform a few years back are quite amenable to real-time time processing and effective analysis today. Stock market time series data are large in volume, and quite often need real-time processing and analysis. Thus it is quite natural that research community has focused on designing and developing robust predictive models for accurately forecasting stochastic nature of stock price movements. This work presents a time series decomposition-based approach for understanding the past behavior of the realty sector of India, and forecasting its behavior in future. While the forecasting models are built using the time series data of the realty sector for the period January 2010 till December 2015, the prediction is made for the time series index values for the months of the year 2016. A detailed comparative analysis of the methods are presented with respect to their forecasting accuracy and extensive results are provided to demonstrate the effectiveness of the six proposed forecasting models.
Keywords: Time Series Decomposition, Trend, Seasonal, Random, Holt Winters Forecasting model, Auto Regression (AR), Moving Average (MA), Auto Regressive Integrated Moving Average (ARIMA), Partial Auto Correlation Function (PACF), Auto Correlation Function (ACF)
JEL Classification: G11, G14, G17, C63
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