Process Data

Use MATLAB^® add-on toolboxes to prepare and process your data for visualization or to trigger actions

You can process channel data to find statistically significant results. For example, you can retrieve financial data for analysis, and use filtering to uncover signals of interest.

Topics

Find Mean of Data Ignoring NaN Values
This example shows how to find the mean of data in a ThingSpeak™ channel while ignoring the NaN values.
Peak Analysis of Data
This example shows how to perform basic peak analysis of data in your ThingSpeak™ channel.
Identify Irregularly Sampled Data
Identify irregularly sampled data in a ThingSpeak™ channel.
Regularize Irregularly Sampled Data
Update timestamps of data in a ThingSpeak™ channel to remove irregularity.
Remove Outliers in Your Data
This example shows how to detect and remove outliers in your ThingSpeak™ channel data using a Hampel filter.

Featured Examples

Remove High-Frequency Noise in Measured Data

Design a low-pass filter and use it to remove high-frequency noise in measured data. High-frequency noise is due to components of a signal varying faster than the signal of interest. Removing high-frequency noise allows the signal of interest to be more compactly represented and enables more accurate analysis. A low-pass filter is a common techqnique for removing high-frequency noise in a signal.

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Retrieve Current Financial Data Using Datafeed Toolbox

Connect to Federal Reserve Economic Data (FRED) and retrieve current financial data.

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Aggregate Data in ThingSpeak Channel

Collect and process data in a ThingSpeak channel

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Remove and Visualize Outliers in Your Data

Detect and remove outliers in your ThingSpeak™ channel data using a Hampel filter. This filter is closely related to a median filter, and it helps to remove outliers from a signal without overly smoothing the data.

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