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.
To use this workflow in KNIME, download it from the below URL and open it in KNIME:
Download WorkflowDeploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.
Try NodePit Runner!Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to mail@nodepit.com.
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.