¶ Description
Time travel.
¶ Parameters
¶ Standard tab
Parameters:
- Columns to process/copy
- Optional: partitioning column
- Rows From the Past (i.e. from 'above')
- Number of rows from the 'Past' to copy
- Number of rows of each partition to skip
- Rows from the past (i.e. from 'below')
- Number of rows from the 'future' to copy
¶ Advanced tab
Parameters:
- Script name
- Short description
- Revision
- Decription
¶ About
This operator is used to create datasets to do time-series prediction. Let's assume that you have the following original dataset:
You could directly use the above dataset to create a predictive model that predict the target value at time T in function of the events (A & B) that occurs at the same time T. ...but you won't get any good predictive model because, most of the time, the target value at time T can only be predicted using the events at time T, T-1, T-2, T-3, ...
TIM creates predictive models that are predicting the Target on a given ROW based on the value contained in the other columns on the SAME ROW, I.e. You need a dataset that contains on each row all the required values to do the prediction for that row.
For example, for time-series prediction, if the target value at time T depends only on events at time T,T-1,T-2 (i.e. the latency of the process to predict is maximum '2'), you should have:
The objective of this operator is to obtain the final "TRANSFORMED TABLE" based on the "ORIGINAL TABLE", so that you can easily use TIM to perform a time-series predictive analysis.
