Stock price prediction has always been a curious, interesting and complex topic in business studies. Stock market is very unreliable for forecasting since there are no major rules or algorithms to estimate or predict share price in the stock market. Several methods like Random Forest analysis, neural networks, time series analysis algorithms like ARIMA, statistical analysis, SVM and many more have been used to predict the stock price of shares in the stock market but not all of these implementations have been correctly identified as a consistent acceptable prediction tool. This paper presents a comparative study of time series analysis using Autoregressive Moving Average i.e. (ARIMA MODEL) and Tableau (a powerful business intelligence tool) to predict the closing index of Google Inc. This paper also presents a process to build stock price prediction model with the help of time series analysis i.e. (ARIMA).The model has been built with the help of R Programming and Tableau. With the upcoming of machine learning and neural networks many researchers are trying to predict the stock price of companies and the trend that it will follow in the near future because it affects the investors as well as the competitors that are present for that company in the market. The prediction of stock price can also be done using neural networks, SVM etc. But here time series analysis has been used because it is easy to implement and it gives better results for short term predictions. The results obtained by ARIMA model shows that it is one of the best methods for the analysis of time series data

Stock market, forecasting, ARIMA Model, Business intelligence

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