Data Smoothing: Definition, Uses, and Methods

What Is Data Smoothing?

Data smoothing is done by using an algorithm to remove noise from a data set. This allows important patterns to more clearly stand out.

Data smoothing can be used to help predict trends, such as those found in securities prices, as well as in economic analysis. Data smoothing is intended to ignore one-time outliers and take into account the effects of seasonality.

Understanding Data Smoothing

When data is compiled, it can be manipulated to remove or reduce any volatility, or any other type of noise. This is called data smoothing.

The idea behind data smoothing is that it can identify simplified changes in order to help predict different trends and patterns. It acts as an aid for statisticians or traders who need to look at a lot of data—that can often be complicated to digest—to find patterns they would not otherwise see.

To explain with a visual representation, imagine a one-year chart for Company X's stock. Each individual high point on the chart for the stock can be reduced while raising all the lower points. This would make a smoother curve, thus helping an investor make predictions about how the stock may perform in the future.

Methods for Data Smoothing

There are different methods in which data smoothing can be done. Some of these include the randomization method, using a random walk, calculating a moving average, or conducting one of several exponential smoothing techniques.

The random walk model is commonly used to describe the behavior of financial instruments, such as stocks. Some investors believe that there is no relationship between past movement in a security's price and its future movement. Random walk smoothing assumes that future data points will equal the last available data point, plus a random variable. Technical and fundamental analysts disagree with this idea; they believe future movements can be extrapolated by examining past trends.

Often used in technical analysis, the moving average smooths out price action while it filters out volatility from random price movements. This process is based on past prices, making it a trend-following—or lagging—indicator. As can be seen in the price chart below, the moving average (EMA) has the general shape and trend of the underlying daily price data, depicted by the candlesticks. The more days incorporated into the moving average, the more smoothed the line becomes.

Advantages and Disadvantages of Data Smoothing

Data smoothing can be used to help identify trends in the economy, in securities, such as stocks, and consumer sentiment. Data smoothing can also be used for other business purposes.

For example, an economist can smooth out data to make seasonal adjustments for certain indicators, like retail sales, by reducing the variations that may occur each month, like holidays or gas prices.

There are downfalls to using this tool, however. Data smoothing doesn't always provide an explanation of the trends or patterns it helps identify. It also may lead to certain data points being ignored by emphasizing others.

Example of Data Smoothing in Financial Accounting

An often-cited example of data smoothing in business accounting is to make an allowance for doubtful accounts by changing bad debt expense from one reporting period to another. For example, a company expects not to receive payment for certain goods over two accounting periods; $1,000 in the first reporting period and $5,000 in the second reporting period.

If the first reporting period is expected to have a high income, the company may include the total amount of $6,000 as the allowance for doubtful accounts in that reporting period. This would increase the bad debt expense on the income statement by $6,000 and reduce net income by $6,000. This would thereby smooth out a high-income period by reducing income. It's important for companies to use judgment and legal accounting methods when adjusting any accounts.

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