Effective for Dynamic-detection(LIDAR/CFAR), Deep Image Sensor, and Time-series records with NaN
entries.
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 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 fits piecewise polynomials between data points, ensuring smooth transitions. It effectively captures complex patterns without the risk of overfitting.
-
Seasonal decomposition is highly effective for time series data with clear seasonal patterns. It separates the data into
trend
,seasonal
, andresidual
components, allowing for targeted imputation. Filling missing values using thetrend
component can be effective in seasonal data.
- 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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Interpolation for Missing Data - Demo (HTML, 3.3MB) Comparison of 6 interpolation models with dataset and illustrations.