Paper Title
Prediction of Stock Market Deviation using ARIMA Algorithm

Abstract
Stock market is an ideal way to invest hard earned money as it has the potential to provide great returns. But, even with the current technology at hand, it is a risky deed due to the inability to understand sudden market changes and interpret data appropriately. To ease the process of investment and to provide better awareness, we propose ‘Prediction of stock market deviation using ARIMA algorithm’: a real-time risk prediction software that considers market interests. It is based on a parametric time series analysis technique- ARIMA (Auto Regressive Integrated Moving Average) algorithm to interpret historic data. It also makes use of Sentiment analysis to convert market trends to valuable information. Since stock market is highly influenced by information release and public acceptance, the addition of Sentiment Analysis to ARIMA boosts system performance and provides a more accurate representation of market volatility. The software provides pictorial and graphical representations and can also be used to compare the growth of two companies for the required time period. The objective is to provide short term and long term prediction capabilities to prepare for future potential investments. Keywords - Natural Language processing, Time series Analysis, ARIMA algorithm, Sentiment Analysis, Dynamic volatility, Forecasting, Stock markets.