# Forecast future values#

With the **Forecast future values** task, you can predict the future values in a
time-series of numerical observations. For example, you can predict future sales
given the records of past ones.

Read the Forecasting tutorial to see an example of application of the forecasting task.

The forecast task expects values to be sorted by date and sampled at regular
intervals, for example, each day, each week, or each month. The **first column**
must contain dates. The next columns must contain one or more series of values.
The **first row** must contain the name of the series. All the series must have
the same number of values/rows. The *Forecast future values* task will predict
values in any row that has a date but doesn’t have values.

Following is an example of a sheet compatible with the forecasting task. The
forecasting task will predict the values of series *A*, *B* and *C* for the
dates *2022-04-05* and *2022-04-06*.

Date |
A |
B |
C |
---|---|---|---|

2022-04-01 |
112 |
125 |
150 |

2022-04-02 |
118 |
149 |
178 |

2022-04-03 |
132 |
170 |
163 |

2022-04-04 |
129 |
170 |
172 |

2022-04-05 |
|||

2022-04-06 |

## Forecasting multiple series#

By default, series (columns) are forecasted independently. Alternatively, it is possible to forecast them together. Predicting series of a similar nature together likely yields more accurate forecasts.

To forecast series together, provide one or more categorical values for each of the series. Series with similar categorical values will be forecasted together using a hierarchical reconciliation algorithm.

Note: The reconciliation algorithm used is a top-down using forecast proportions.

The next example shows the sales of 5 different products. Under the product name, the type, and the category of each product are indicated in the first two rows.

Date |
Lemonade |
Orange Juice |
White Wine |
Analog watch |
Digital watch |
---|---|---|---|---|---|

Category |
drink |
drink |
drink |
wearable |
wearable |

Type |
soda |
soda |
wine |
watch |
watch |

2022-04-01 |
112 |
125 |
243 |
150 |
211 |

2022-04-02 |
118 |
149 |
264 |
178 |
180 |

2022-04-03 |
132 |
170 |
272 |
163 |
201 |

2022-04-04 |
129 |
170 |
237 |
172 |
204 |

2022-04-05 |
|||||

2022-04-06 |

Here, *Lemonade* and *Orange Juice* are both *soda* and *drink*. Both products
are serving the same purpose (those are called *substitution products*) and
probably have similar sales trends (e.g., increase sales in summer). *White
Wine* is also a *drink*, but it is not a soda.

## How are values forecasted#

The algorithm used is a tuned model with cleaning, holiday, seasonality and
trend structure. Look at the *Advanced* option section for the *Forecast future
values* task. Details of the algorithms are described in
Forecasting: Principles and Practice by
Hyndman et al.