REQUIRED |
KEY |
TYPE |
DESCRIPTION |
Yes |
deploymentToken |
str |
The deployment token to authenticate access to created deployments. This token is only authorized to predict on deployments in this project, so it is safe to embed this model inside of an application or website.
|
Yes |
deploymentId |
str |
The unique identifier to a deployment created under the project.
|
Yes |
queryData |
dict |
This will be a dictionary where 'Key' will be the column name (e.g. a column with name 'store_id' in your dataset) mapped to the column mapping ITEM_ID that uniquely identifies the entity against which forecasting is performed and 'Value' will be the unique value of the same entity.
|
No |
futureData |
List[dict] |
This will be a list of values known ahead of time that are relevant for forecasting (e.g. State Holidays, National Holidays, etc.). Each element is a dictionary, where the key and the value both will be of type 'str'. For example future data entered for a Store may be [{"Holiday":"No", "Promo":"Yes", "Date": "2015-07-31 00:00:00"}].
|
No |
numPredictions |
int |
The number of timestamps to predict in the future.
|
No |
predictionStart |
str |
The start date for predictions (e.g., "2015-08-01T00:00:00" as input for mid-night of 2015-08-01).
|
No |
explainPredictions |
bool |
Will explain predictions for forecasting
|
No |
explainerType |
str |
Type of explainer to use for explanations
|
No |
getItemData |
bool |
Will return the data corresponding to items in query
|
Note: The arguments for the API methods follow camelCase but for Python SDK underscore_case is followed.