Due to the fast-paced and changing demands of their users, computing systems require autonomic resource management. To enable proactive and accurate decision-making for changes causing a particular overhead, reliable forecasts are needed. In fact, choosing the best performing forecasting method for a given time series scenario is a crucial task. Taking the “No-Free-Lunch Theorem” into account, there exists no forecasting method that performs best on all types of time series. To this end, we propose an automated approach that (i) extracts characteristics from a given time series, (ii) selects the best-suited machine learning method based on recommendation, and finally, (iii) performs the forecast. Our approach offers the benefit of not relying on a single method with its possibly inaccurate forecasts. In an extensive evaluation, our approach achieves the best forecasting accuracy.
@inproceedings{bauer2020seasonalforecast,
title = {{An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series}},
author = {Bauer, André and Züfle, Marwin and Grohmann, Johannes and Schmitt, Norbert and Herbst, Nikolas and Kounev, Samuel},
year = {2020},
month = apr,
day = {20--24},
booktitle = {Proceedings of the 11th ACM/SPEC International Conference on Performance Engineering (ICPE)},
publisher = {ACM},
pages = {48--55},
url = {https://dl.acm.org/doi/abs/10.1145/3358960.3379123},
numpages = {8}
}