R provides many features to analyze and visualize time-series data. Time series modeling and forecasting are used for various business applications, such as exploring sales patterns and trends, predicting future sales and detecting unusual phenomena. This allows companies to reduce risk, increase profit and achieve cost-effectiveness. Several machine learning algorithms exist that can be used for forecasting, some of which already exist in the forecast package in R. In this paper, we aim to explore some of the commonly used techniques for predictions.