One of the best, and most versatile, tools when analyzing data are trend lines. Trendlines capture the aggregate behavior of a data set and are useful in a variety of applications ranging from Ad Campaign Optimization to Data Auditing. They can help you identify problems in your business, or predict the future.
Consider this data, which like much real world data is very noisy:
Now, look at that same data with a trendline:
As you can see, the trendline captures the aggregate movement of the data making it much easier to see the overall trend.
There are many different mathematical approaches for creating trend lines, ranging from simple (moving average) to complex (ARIMA) and the approach that works best for your data depends largely on the type and nature of your data. Choosing the right approach can be the difference between an informative trendline and a very misleading faux trend.
This week we will survey a variety of ways you can create trend lines and when each approach might be appropriate. Specifically we will cover:
- Part 2 – Moving Averages
- Part 3 – Linear and Polynomial Regression
- Part 4 – ARIMA
- Part 5 – When Not to Use Trendlines
Tomorrow we’ll get started with the simplest form of trendline, moving averages.
Quote of the Day: “Follow the trend lines, not the headlines.” ― Bill Clinton