Energy Efficiency Evaluation of Frameworks for Algorithms in Time Series Forecasting

Abstract

In this study, the energy efficiency of time series forecasting algorithms is addressed in a broad context, highlighting the importance of optimizing energy consumption in computational applications. The purpose of this study is to compare the energy efficiency and accuracy of algorithms implemented in different frameworks, specifically Darts, TensorFlow, and Prophet, using the ARIMA technique. The experiments were conducted on a local infrastructure. The Python library CodeCarbon and the physical energy consumption measurement device openZmeter were used to measure the energy consumption. The results show significant differences in energy consumption and algorithm accuracy depending on the framework and execution environment. We conclude that it is possible to achieve an optimal balance between energy efficiency and accuracy in time series forecasting, which has important implications for developing more sustainable and efficient applications. This study provides valuable guidance for researchers and professionals interested in the energy efficiency of forecasting algorithms.