1. PlanIQ
  2. PlanIQ glossary

Here are some terms and definitions for PlanIQ.

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Static categorical metadata that describes the items for which a forecast should be generated. The attributes often represent item hierarchy elements or business dimensions (for example, SKU by store). Attributes is an optional data set. They help PlanIQ discover patterns across similar items and group items by their shared characteristics.
Examples: Region, Category, Style.

Data collection

An entity that contains the data used to train forecast models and generate forecasts. A data collection includes historical data and can include related data and/or attributes.

Forecast action

An action generates forecasts using a pretrained forecast model and imports the results into an Anaplan model. Forecast actions can be executed on demand or scheduled.

Forecast horizon

The length of time into the future for which forecasts should be generated. The maximum forecast horizon depends on the length of the historical data and the selected algorithm.

Forecast model

An algorithm trained on data from a data collection to support future forecasts.

Forecast time interval

The time granularity of the forecasted data points as dictated by the input data (historical data and related data). The forecast time interval can be either daily, weekly, or monthly.

Historical data (actuals)

Numeric past time series data of the items for which a forecast should be generated. As the main data type in time series forecasting, the historical data is mandatory.
Examples: Units Sold, Expenditure.

Related data 

Numeric time series data related to the items for which a forecast should be generated. Related data includes historical data points leading up to the forecast horizon, and can also include future data points to cover the length of the forecast horizon. The related data is optional. Related data can help PlanIQ improve the forecast accuracy.
Examples: Price, Promotion.

Time series

A sequence of successive data points (observations) in time, typically in equally spaced time intervals.

Time series forecasting

The prediction of future values of a time series based on historical data and other data types.