Solution to the Exercise 4: ARIMA Models

This workflow predicts the irregular component of time series (energy consumption) by autoregressive integrated moving average (ARIMA) models that aim at modeling the correlation between lagged values and controling for seasonality in time series. The number of lagged values considered in the model can be set manually, or it can be optimized by testing different combinations of AR, I, and MA components of the model. The irregular component of time series is what is left after removing the trend and first and second seasonality.

This is a companion discussion topic for the original entry at

I have been trying to use the workflow proposed, as I try to install the mentioned extensions. this message keeps popping up:
Unable to read repository at is not a valid repository location.
similar messages I am receiving for the links that are embedded in the workflow.

is there any other option or workflow can be used to run ARIMA decompose signal and auto learner.
thank you

Hello @Ahmad2591988,

welcome to KNIME Community!

How did you try to install Extensions? See this section of a guide on how to do it: