decide which one to use. Parameter tuning often uses cross-validation[@kohavi1995study], which bases on the separation of data-sets for training and for validation. The fewer parameters a model has, the less flexible it is, and the less likely it is that overfitting will take place. Therefore the *Akaike Information Criterion* (AIC)[@akaike1974new] was defined to include the number of parameters into the quality of the model: $$\label{eq:aic_definition}
    AIC = 2k - 2\ln(RSS).$$ The integer $k$ gives the number of parameters and the residual sum of squares (RSS) quantifies the prediction error[@ManganModelSelection.2017]. Therefore, the AIC is a trade-off between prediction and model complexity.\
In order to be able to compare errors between different time series, the Mean Absolute Scaled Error *MASE*[@Hyndman.2006] can be used, which relates the error to the overall change of the time series. For a small number of samples, there is a special correction term for the AIC. This so called AICc is defined in Eq. following[@Burnham.2002 Chap. 6.8]. $$\label{eq:aicc}
    AICc = AIC + \frac{2k^2 + 2k}{n-k-1}.$$ The best model achieves the smallest AIC, or AICc respectively. The term $\Delta_j = AIC_i - AIC_{min}$ is a measurement for the difference between models. For $\Delta_j < 2$, models are similar, whereas 