Impact analysis is when we analyze your historical time series data and determine the impact of internal and external factors. You can use the predictive power of impact analysis to evaluate the impact of an event, promotion, limited time offer, etc. on your bottom line, determine impact of weather or illness on productivity, or understand how holidays will impact sales.
Both the start and end dates for the impact session must always be on or before the specified timestamp of the last record in your dataset.
To understand how to submit data, you can read more in the Sending Data article.
Here are the parameters you can include in either the query string or the body of a POST request to create an Impact Analysis Session:
dataSourceName- Name of the data source (dataset or view) for which to determine impact
targetColumn- Column in the specified dataset for which to determine impact
eventName- Name of the event for which to determine impact
resultInterval- The interval at which predictions should be generated. Possible values are
Year. Defaults to
startDate- Format date-time (as date-time in ISO8601). First date of the event
endDate- Format date-time (as date-time in ISO8601). Last date of the event
callbackUrl- The Webhook URL that will receive updates when the Session status changes
Here are the differences:
eventNameso you can create a friendly name for the intervention period.
pValue- Statistical value used to determine the significance of the impact. A small p-value indicates strong evidence of impact, whereas a p-value approaching 0.5 indicates weak evidence of impact.
absoluteEffect- Total absolute effect of the event on the dataset. Answers the question, “How much did this event affect my dataset?”
relativeEffect- Percentage effect of the event on the dataset. Answers the question, “By what percentage did this event affect my dataset?”
IMPORTANT: If you want to find the impact a particular feature had on the target value, you must set that feature’s value off during the dates of the intervention period otherwise it will not measure the impact correctly. Impact session results consist of the predictions of what would have happened over the specified date range, had the impactful event not occurred, which is why that feature should be removed from the intervention period.