What Is a Detrend?
A detrend involves removing the effects of trend from a data set to show only the differences in values from the trend; it allows cyclical and other patterns to be identified. This is done using regression analysis and other statistical techniques. Detrending helps to paint a clearer picture of the pattern that you’re looking to identify.
- Detrending is used to identify cyclical and other patterns in a particular data set.
- There are typically two classes of trends: deterministic and stochastic.
- Before detrending can occur, the type of trend needs to be identified.
- Detrend price oscillator is the simplest method that can be used to detrend.
- There are several other methods that may be used in certain circumstances, but they are often more difficult and complicated.
Understanding a Detrend
When a researcher detrends a particular data set, they are typically doing so in order to remove the aspect that appears to be causing some kind of distortion in the final outcome. There are often major benefits to removing trend information from a data set, as there is with simply identifying the trends in the first place and modeling ones that have proven to be useful or otherwise informative in the past.
Removing a trend from your data set can allow you to focus instead on the fluctuations and identify any number of important factors. This is particularly useful in sales and marketing.
Types of a Detrend
Different charting services include the use of a detrend price oscillator, which gives traders a method for analyzing shorter-term cyclical patterns. These patterns can then be used to more effectively identify major turning points in the longer-term cycle.
There are several other methods that can be used to detrend, but the majority of them are far more complex and difficult to use. A few of the alternative options are quadratic detrending, using the Baxter-King filter (for moving average trend lines only), and using the Hodrick-Prescott filter (for cyclical components of a particular time series only).
Which method is the best for the project and data at hand will depend on numerous individual factors, including the particular field of study and whether or not the data is linearly correlated. The option to detrend quickly and efficiently is included in the majority of statistical software packages that are available and widely used today.
Requirements for a Detrend
Before detrending can occur, the particular class of the trend must be identified in order to determine the most appropriate method to be used. While there are many different kinds of trends, they typically occur within only two different classes. These classes are deterministic trends and stochastic trends.
The deterministic trends consistently decrease or increase, and the stochastic trends inconsistently decrease or increase. Deterministic trends are often easier to identify and detrend since they are a bit more predictable and reliable, but there are methods that have also proven useful for stochastic trends, as well.
Example of Detrending
Often times market momentum will carry pricing trends. From around 2011-2015, there was a large low-quality trend in the U.S. equity markets. Stocks from issuers that had lower quality fundamentals than your classic blue-chip companies outperformed by a wide margin. This data, if not "detrended" from forecasting models, might have created false positives for market tops or other economic turning points.
One of the most common uses of detrending is in a data set that shows some kind of overall increase. Detrending the data will allow you to see any potential subtrends, which can be incredibly useful for scientific, financial, sales, and marketing research across the board.