Detecting and Removing Outliers in Signal Processing and Data Analysis

In the fields of signal processing and data analysis, the presence of outliers (also known as anomalous values or aberrant points) can significantly impact analytical results. MATLAB provides a powerful suite of tools and methods to identify and handle these abnormal data points.

Common outlier detection methods include statistical approaches like the 3σ criterion, Interquartile Range (IQR) method, and moving window detection. The 3σ criterion assumes data follows a normal distribution and identifies points beyond the mean ±3 standard deviations as outliers. The IQR method calculates the first and third quartiles to establish normal data boundaries, considering values outside this range as anomalies.

When implementing outlier removal in MATLAB, an iterative approach can progressively detect and eliminate abnormal values. Initial smoothing of raw data helps distinguish true outliers from normal fluctuations. Implementation typically involves comparing original data with smoothed versions to flag suspicious points using functions like smoothdata() or movmean().

Multiple strategies exist for handling outliers: direct deletion, replacement with neighboring data averages/medians, or interpolation methods. The choice depends on application requirements and data continuity needs, implementable through functions such as filloutliers() with various filling methods like 'previous', 'linear', or 'spline'.

Practical applications often require multiple iterations and parameter tuning to balance preserving valid information and removing noise. Visualization tools like data plotting (using plot() or scatter()) are crucial for verifying outlier processing effectiveness and refining detection thresholds.