Imputational trace with classical interpolation (Linear/Polynomial/Spline/Seasonal) and Machine Learning Methods.

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• [Basic] - Linear Interpolation
• [Advanced] - Curved Interpolation:
  • Polynomial Interpolation
  • Spline Interpolation
• Dealing with Seasonal Data:
  • Seasonal Decomposition of Time Series
  • Filling Missing Values Using Seasonal Decomposition
• Machine Learning for Imputation:
  • Using K-Nearest Neighbors to Impute Missing Values
• Notes for Data Engineers
• Demo & Comparison
  • Interpolation for Missing Data - Detailed Demo (click Download to View)

Linear interpolation is a simple and effective method for estimating missing values when the data is relatively smooth, and exhibits linear trends between data points.

• Polynomial Interpolation

  Polynomial interpolation fits a polynomial of specified order, or complex curves through the known data points, making it suitable for datasets with non-linear trends.

• Spline Interpolation

  Spline interpolation fits piecewise polynomials between data points, ensuring smooth transitions. It effectively captures complex patterns without the risk of overfitting.

• Seasonal Decomposition of Time Series

• Filling Missing Values Using Seasonal Decomposition

  Seasonal decomposition is highly effective for time series data with clear seasonal patterns. It separates the data into trend, seasonal, and residual components, allowing for targeted imputation. Filling missing values using the trend component can be effective in seasonal data.

• Using K-Nearest Neighbors to Impute Missing Values

  KNN imputation is a versatile method that estimates missing values based on the similarity to other data points. It can capture local patterns in the data.

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• Download to View:

Interpolation for Missing Data - Demo (HTML, 3.3MB) Comparison of 6 interpolation models with dataset and illustrations.

Python solution - Linear Interpolation (.py)

Python solution - Polynomial Interpolation (.py)

Prepared & Published by:

Sun CHUNG, SMIEEE M.Sc. HKU - colab w/ MIT-IDSS