Environmental Cost Function for Time Series Models: The M4 Competition

In this work, a cost function is estimated for eight models from the M4 competition. The main objective of the M competitions is to evaluate the accuracy of numerous forecasting models. This study introduces metrics to measure the environmental cost associated with running different time series models during the training and forecasting phases. This approach enables the construction of an environmental cost function that depends on other explanatory variables. Interpretable models help identify key drivers of environmental impact, while more complex machine learning models are used to predict emissions without rerunning the algorithms. The findings contribute to Green AI by promoting the evaluation of forecasting models not only by forecasting precision but also by sustainability.