10% Missing Data? Here's How K-Nearest Neighbors Can Save Your Time-Series Analysis

In time-series analysis, missing data is a common issue. While simple imputation methods and regression models like linear regression and decision trees can address this, they often fall short with complex data patterns. Enter K-Nearest Neighbors (KNN), a technique that excels in capturing subtle fluctuations in time-series data. KNN makes few assumptions about the data’s relationships, making it a versatile tool for imputation. This article uses a mock energy production dataset with 10% of its values randomly missing to demonstrate KNN’s effectiveness. By following along, you can apply these techniques in real-time, enhancing your ability to handle missing data in your own analyses.

Source: towardsdatascience.com