Handling Missing Values

Conventional SVD algorithms aren’t typically designed to manage missing values. To address this, xeofs handles missing values (NaNs) within your data. There are two primary types of missing values:

1. Full-dimensional: NaNs spanning all samples for a specific feature or vice versa.

2. Isolated: Occasional or sporadic NaNs within the dataset.

For example, in a 3D dataset with dimensions (time, lon, lat), a full-dimensional NaN might represent a grid point (lon, lat) exhibiting NaNs across all time steps. Conversely, an isolated NaN might indicate a grid point (lon, lat) displaying NaNs for only certain time steps.

xeofs is adept at handling full-dimensional NaNs. However, it cannot manage isolated NaNs. Users need to decide how to fill or remove features or samples containing isolated NaNs.

Note

xeofs provides an optional runtime check check_nans, enabled by default, which raises an error if isolated NaNs are detected.