Prediction of selected Indian stock using a partitioning–interpolation based ARIMA–GARCH model

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2015
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Abstract
Accurate long-term prediction of time series data (TSD) is a very useful research challenge in diversified fields. As financial TSD are highly volatile, multi-step prediction of financial TSD is a major research problem in TSD mining. The two challenges encountered are, maintaining high prediction accuracy and preserving the data trend across the forecast horizon. The linear traditional models such as autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroscedastic (GARCH) preserve data trend to some extent, at the cost of prediction accuracy. Non-linear models like ANN maintain prediction accuracy by sacrificing data trend. In this paper, a linear hybrid model, which maintains prediction accuracy while preserving data trend, is proposed. A quantitative reasoning analysis justifying the accuracy of proposed model is also presented. A moving-average (MA) filter based pre-processing, partitioning and interpolation (PI) technique are incorporated by the proposed model. Some existing models and the proposed model are applied on selected NSE India stock market data. Performance results show that for multi-step ahead prediction, the proposed model outperforms the others in terms of both prediction accuracy and preserving data trend.
Reference Key
babu2015predictionapplied Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Babu, C. Narendra;Reddy, B. Eswara;
Journal applied computing and informatics
Year 2015
DOI
DOI not found
URL
http://www.sciencedirect.com/science/article/pii/S2210832714000258
Keywords
Biology (General) Information technology environmental effects of industries and plants renewable energy sources environmental sciences economics as a science agriculture finance social sciences economic theory. demography probabilities. mathematical statistics

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