Due to recent advancements in technology the stock prices prediction techniques are widely used and became so crucial for investors, portfolio managers and financial analysts to increase their accuracy in prices prediction. The Study mainly describes the comparision of different time series forecasting models which are used for the stock prices prediction, mainly considering on IT domain for four major based stocks listed on the Indian Stock Exchange with large market capitalization. The analysis mainly focuses on the integration of fundamental analysis which includes the financial indicators such as revenue, net profit, and balance sheet data, ratios and with time series models for enhancing the precision of long term stock price predictions. Advanced neural network architectures which are highly oriented towards predicting long time prices are used, Models which are more suitable for time series analyses are used in this analysis to predict future prices of stocks. The fundamental data related to four companies are helpful to train the model and the model evaluation is done with with the metric known as Mean Absolute Percentage Error (MAPE). The results shows the strengths and weaknesses of each model in stock price forecasting for each company, especially explaining how fundamental data is useful for the analysis.