Forecasting vs. Prediction: The Essential Differences Between Time Series and Regression Analysis

Time series and regression are most commonly used in business environments. Both have their unique advantages. In this tutorial, you can learn the essential difference between the two analysis.

Time series analysis

Time series is most commonly used to learn about the trends. What is a general trend, and how is a trend divided into a long time? For example, you are concerned about the cycles of history. You have planned your strategy based on a 5, 10, or 500-year cycle.

If you look at history, you can find that over time there have been many replacements and establishments of various events. Time series analysis is always used to predict the future based on historical moments. People use time series to build the connection between the history and the future. They mostly care about planning better for the future on the evidence of the present moments.

Why people choose time series because:

1. They believe that history always repeats itself.
2. The previous experiences can reflect the future.

Regression analysis?

If time series analysis is based on time, the regression analysis is based on two variables. For example, you are judging the X, Y, and Z variables based on A. Technically, we are going to learn the relationship between the independent variables and dependent variables. In this analysis, statistical tools are used to estimate the quantitative relationship between the two variables. By examining the independent variable, we can predict the outcome of the dependent variable.

In summary, time series analysis is used to understand the past and predict the future, whereas in regression analysis we will build the numerical relationship between a group of random variables and extract the results.