Time series analysis refers to methods used to analyze trends in data over time. There are several types of trends including stock, flow, geographic, temporal, and intuitive. Time series analysis helps organizations understand underlying causes of trends to predict future changes. Components of time series include secular trend, seasonal trend, cyclical variations, and irregular variations. Models for time series include addition and multiplication models to represent original data and remove trend, seasonal, and cycle fluctuations. Common methods to measure trends are freehand, semi-averages, and moving averages.