Time series forecasting is a method of predicting future behavior based upon historical behavior. Both the academic and business realms use these types of models frequently in order to describe the world. One reason time series forecasting is popular is because all you essentially need is historical data on the phenomena you are trying to describe.

This type of model is built around the premise that the past behavior of a variable will predict the future behavior of said variable. However, it is assumed that there is random error in the historical data. Hence, the goal of the analysis is to remove the error from the pattern's trend.

Time Series Forecasting Reduces Uncertainty

Understanding past trends is one manner to reduce uncertainty. Using time series forecasting in business is a way for companies to plan for the future. These analyses can describe a wide range of behaviors, ranging from monthly sales data to hourly bandwidth usage.

In order to get useable results, it is important to properly specify your time series forecasting model. If you are unsure of how to do this, a statistician can assist you. A statistician can help you develop your model, analyze the data, and interpret your results.