This Project aims at performing time series analysis for four different time series such as JPMorgan Chase & Co., Paypal, Visa and Wells Fargo & Company. The time series data was fetched, cleaned and transformed for analysis. Segmentation was performed on the data for better understanding. It was observed that the count of segments increased with decreasing mean square error tolerance level. Ultimately, a linear regression model was used to predict the next day value of a selected time series. It was observed that the predictions were made with acceptable accuracy. This can be attributed to the fact that the selected time series belong to the same industry thereby having a correlation among them.