Interface DeploymentsTimeSeriesForecastParams

A payload of data matching schema. We assume the following about your data:

• All timeseries are of equal length and are uniform in nature (the time difference between two successive rows is constant). This implies that there are no missing rows of data;
• The data meet the minimum model-dependent historical context length which can be any number of rows per timeseries;

Note that the example payloads shown are for illustration purposes only. An actual payload would necessary be much larger to meet minimum model-specific context lengths.

Exogenous or supporting features that extend into the forecasting horizon (e.g., a weather forecast or calendar of special promotions) which are known in advance. future_data would be in the same format as data except that all timestamps would be in the forecast horizon and it would not include previously specified target_columns.

The id_or_name can be either the deployment_id that identifies the deployment or a serving_name that allows a predefined URL to be used to post a prediction.

The WML instance that is associated with the deployment will be used for limits and billing (if a paid plan).

The parameters for the forecast request.

Contains metadata about your timeseries data input.