In the real world, users deal with raw data that contains noise or deviation. The representation of this noisy data may ill-depict the trend, so it becomes essential to remove noise from data in Excel. Moving Average and Exponential Smoothing flatten the deviation by removing the noise within it.

Letâ€™s say we have velocity data against time and want to remove noise from the velocity data.

**Remove Noise from Data in Excel: ****2 Easy Ways **

Smoothing data and removing noise from data are similar things. Smoothing raw data removes existing noise. This smoothed data is crucial to understanding prevailing trends and better forecasts.

Go through the below sections to be able to remove noise from data.

**Method 1: Using Moving Average to Remove Noise from Data**

From the dataset, we get the existing average velocity values for a certain interval. Now, we need to find the Moving Average values to smoothen the **Velocity-Time** curve.

- Type the following formula into the cells of the
**D**Column.

`=AVERAGE(C4:C8)`

- Highlight all three columns, then go to the
**Insert**tab. - Choose a
**Scatter Chart**type.

- Instantly, Excel inserts a
**Scatter Chart**as shown in the below picture.

- Furnish the chart according to your taste, as depicted below.

The above picture shows the smoothing of noisy data. As the moving average takes the following couple of entries (i.e., **3**, **5**, or **7**) to come up with a smooth entry, the **Moving Average** curve doesnâ€™t have the last couple of values. This shortcoming can be overcome using **Trendline **insertion or **Data Analysis Tools**.

**Trendline Insertion**

What if we donâ€™t want to use **the AVERAGE function** or want automatic smoothing of an inserted curve?

- Right-click on the raw data scatters curve. The Context Menu appears.
- Select
**Add Trendline**.

- Excel pops up the
**Format Trendline**side window. Mark**Moving Average**under**Trendline Options**and enter 5 in the Period command box.

- The
**Moving Average Trendline**gets inserted.

**Moving Average â€“ Data Analysis Tools**

If users want all the smoothened data, they can use the **Moving Average Tools** in **Data Analysis**.

- Move to the
**Data**tab. - Click
**Data Analysis**.

- The
**Data Analysis**dialog box appears. Select**Moving Average**under**Analysis Tools**. - Click
**OK**.

- Clicking
**OK**fetches the**Moving Average**dialog box. Assign Input (i.e.,**C4:C20**) and**Output**(i.e.,**D4**) Ranges. - Finally, click
**OK**.

- Excel inserts all the moving average values, removing noise.

Use the smoothed data to draw charts or any kind of representation to solidify your stance to some extent.

**Method 2: Fetching Smoothed Data Using Exponential Smoothing**

As an alternative to** method 1**, users can exercise** Exponential Smoothing Tools** from **Data Analysis**.

- Go to
**Data**>**Data Analysis**. Excel displays the**Data Analysis**. - Choose
**Exponential Smoothing**under**Analysis Tools**. - Click
**OK**.

- Upon Excel fetching the
**Exponential Smoothing**dialog box, assign the**Input**(i.e.,**C4:C20**),**Output**(i.e.,**D4**) Ranges and**Damping Factor**(i.e.,**0.91**). - Click
**OK**.

- Excel stacks the smoothed data in the
**D**column.Â Highlight all three columns. - Move to the
**Insert**tab. - Select a
**Scatter Chart**.

- In a second, Excel inserts a scatter chart containing noisy and smoothed data.

- Modify the
**Chart**and youâ€™ll get a depiction like a picture below.

**Things to Keep in Mind**

- Moving Average method has alternatives as smoothed data can be achieved using Formulas or Data Analysis Tools.
- Signal processing or smoothing signals is similar to removing noise from data if signals contain numeric values.
- The Trendline of Exponential Smoothing returns the same curve to Data Analysis- Exponential Smoothing in the chart.

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**Conclusion**

This article demonstrates the ways to remove noise from data in Excel. Though the moving average method has multiple alternatives for its execution depending on the outcome, exponential smoothing has the same outcome irrespective of ways.

hi,

what is the consideration to put 0.91 as Damping Factor?

Hi

BAGUS,In the context of Exponential Smoothing in Excel’s Data Analysis Toolpak, the

Damping Factorrefers to a parameter used to control the impact of older observations on the forecasted values.The damping factor has a value between

0and1. It determines the weight assigned to the most recent observation when calculating the forecast. A higher value (closer to 1) gives more weight to the most recent data point, making the forecast more responsive to recent changes in the data. On the other hand, a lower value (closer to 0) gives less weight to the most recent data point, making the forecast more stable and less responsive to short-term fluctuations.The value of

Damping Factorbeing0.91suggests relatively high importance to the most recent data while still considering some historical data. The choice of0.91is somewhat arbitrary and depends on the specific characteristics of the data and the desired balance between responsiveness and stability in the forecast. Different values of the damping factor may be chosen based on the analyst’s judgment and the nature of the data being analyzed.To determine the most appropriate value for the

Damping Factor, it’s often a good practice to experiment with different values and evaluate the forecast accuracy using techniques like Mean Absolute Error (MAE) or Mean Squared Error (MSE). The choice ofDamping Factorshould ideally be data-driven and selected based on how well it performs in forecasting historical data or predicting future values.Regards

Rafiul HasanTeam

ExcelDemy