# Concepts

## Multi-dimensional datasets

In the context of DBnomics, a multi-dimensional dataset is a group of time series where each one is categorized using dimensions.

Let's start from the following hypothetical CSV file named `product_prices.csv` that tracks the evolution of the price of different products in different countries:

| sku | country | year | price |
| --- | ------- | ---- | ----- |
| 111 | FR      | 2000 | 12    |
| 111 | FR      | 2001 | 13    |
| 111 | FR      | 2002 | 11    |
| 111 | DE      | 2001 | 9     |
| 111 | DE      | 2002 | 11    |
| 111 | DE      | 2003 | 14    |
| 222 | FR      | 2000 | 87    |
| 222 | FR      | 2001 | 88    |
| 222 | FR      | 2002 | 90    |
| 222 | FR      | 2003 | 79    |
| 333 | FR      | 2000 | 23    |
| 333 | FR      | 2001 | 22    |
| 333 | FR      | 2002 | 23    |
| 333 | FR      | 2003 | 21    |

This CSV file can be turned into a multi-dimensional dataset with the code `PRODUCT_PRICES`.

We can infer 2 dimensions: `SKU={111,222,333}` and `COUNTRY={DE,FR}`.

The dataset is composed of 4 time series, each being related to a single product and country, as each dimension must be set with a single value:

Series `111.FR`:

| period | value |
| ------ | ----- |
| 2000   | 12    |
| 2001   | 13    |
| 2002   | 11    |

Series `111.DE`:

| period | value |
| ------ | ----- |
| 2001   | 9     |
| 2002   | 11    |
| 2003   | 14    |

Series `222.FR`:

| period | value |
| ------ | ----- |
| 2000   | 87    |
| 2001   | 88    |
| 2002   | 90    |
| 2003   | 79    |

Series `333.FR`:

| period | value |
| ------ | ----- |
| 2000   | 23    |
| 2001   | 22    |
| 2002   | 23    |
| 2003   | 21    |

Note: because the dimensions of a dataset are ordered, we can infer the series codes by concatenating the codes of the values of the dimensions, separated by a `.` character.
For example, the series for `SKU=111` and `COUNTRY=FR` has the code `111.FR`.