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.
Used extensions & nodes
Created with KNIME Analytics Platform version 4.1.1
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