So here is what is now an everyday example of representing multidimensional data on a two dimensional surface.

Unfortunately, they often become inadequate when dealing with massive multidimensional data.

It preserves the structure and correlation in the original data before projection by operating on natural tensorial representation of multidimensional data.

Projection pursuit is a type of statistical technique which involves finding the most "interesting" possible projections in multidimensional data.

OLAP tools enable users to analyze multidimensional data interactively from multiple perspectives.

Drilling down provides a method of exploring multidimensional data by moving from one level of detail to the next.

For multidimensional data, tensor representation can be used in dimensionality reduction through multilinear subspace learning.

Multilinear subspace learning algorithms (predicting labels of multidimensional data using tensor representations)

Data drilling (also drilldown) refers to any of various operations and transformations on tabular, relational, and multidimensional data.

However, blind signal separation is now routinely performed on multidimensional data, such as images and tensors, which may involve no time dimension whatsoever.