Description

Data imputation is a statistical technique used to replace missing data with substitute values. This process helps to maintain the majority of the dataset’s information, preventing the loss of valuable data.

How it Works

Data imputation works by estimating missing values based on the observed data. The imputation process can be univariate, using only non-missing values in the same feature dimension, or multivariate, using the entire set of available feature dimensions to estimate the missing values.

Benefits

• Preserves more information and variation in the dataset.
• Reduces the risk of introducing bias or distortion.
• Allows the use of methods or tools that require complete datasets.
• Prevents errors that can occur due to missing data when using certain machine learning libraries.

Limitations

• May introduce bias if not done correctly.
• Can underestimate the variability in the data.
• May not provide accurate imputations for data with complex patterns or seasonality.
• The effectiveness of the imputation can be dependent on the distribution of the data.

Features

• Can be applied to any time series data with missing values.
• Can handle univariate and multivariate time series data.
• Can incorporate information about trends and seasonality in the data.

Use Cases