Accessing, Transforming and Modeling Time Series

This workflow shows how to access time series data, make it equally-spaced, impute missing values, aggregate it at a greater granularity, and explore it visually. After these steps, the time series is decomposed into trend, seasonality, and residual. The residual is modeled with an ARIMA model, and deployment data are saved for testing the model's out-of-sample forecast accuracy.

This is a companion discussion topic for the original entry at

hi there,

how i can have the data’s example?
thank you


Hello @SimoneDePaoli,

if haven’t found input data is in _data folder one level up.


Hi there!
I try to create Time Series Analysis with Components.
But i have problem zu execute the node Decompose Signal. Is there any tricks that i need to know?
Thanks a lot!

Hi @Loknica07 -

Maybe check this thread in the main AP forum to see if that helps you:


Hello, I got an error running this workflow with the superstore data. The Auto ARMINA learner node failed -
“ERROR Python Learner 3:79:0:105 Execute failed: name ‘i_out’ is not defined
Traceback (most recent call last):
File “”, line 47, in
NameError: name ‘i_out’ is not defined”

I can see i_out defined in the Python learner node in the component, but I don’t know Python very well. Can anyone help?

Hi @iTree,

Are you still encountering this error?

I started another forum post to hopefully get this resolved here: