Stock Price Forecasting Using Data from Yahoo Finance and Analysing Seasonal and Nonseasonal Trend

The stock market is an aggregation of individuals and corporations engaged in a loose network of buying and selling of shares of companies, called stocks. This buying and selling of shares have high risk associated with it, and therefore several predictions are made to avoid losses and incur profits. Time series analysis is the most basic and fundamental technique to perform this task. This paper depicts the broader view of stock price prediction by combining the results of the different time series analysis model, to find a range of stock prices for the buyers in which no losses will be incurred. The proposed work aims to find the relationship between the stock prices and different existing time series algorithms namely ARIMA and Holt Winter, to explore a safe range of stock prices for investments and thus improving the accuracy of prediction. The results of this work show that these predictions will be equally effective if done for a long or short time intervals.