%0 Conference Paper %1 ZBBHCK2017-ITISE-Telescope %A Züfle, Marwin %A Bauer, André %A Herbst, Nikolas %A Curtef, Valentin %A Kounev, Samuel %B Proceedings of the International work-conference on Time Series (ITISE 2017) %D 2017 %K Online_monitoring_and_forecasting Optimization Prediction Statistical_estimation_and_machine_learning Telescope Tool descartes t_extendedabstract %T Telescope: A Hybrid Forecast Method for Univariate Time Series %X Forecasting is an important part of the decision-making process and used in many fields like business, economics, finance, science, and engineering. According to the No-Free-Lunch-Theorem from 1997, there is no general forecasting method, that performs best for all time series. Instead, expert knowledge is needed to decide which forecasting method to choose for a specific time series with its own characteristics. Since a trial and error approach is very inefficient and expert knowledge is useful but a time-consuming task that cannot be fully automated, we present a new hybrid multi-step-ahead forecasting approach based on time series decomposition. Initial evaluations show that this hybrid approach improves the forecast accuracy compared to six existing forecasting methods while maintaining a short runtime.

@inproceedings{ZBBHCK2017-ITISE-Telescope, abstract = {Forecasting is an important part of the decision-making process and used in many fields like business, economics, finance, science, and engineering. According to the No-Free-Lunch-Theorem from 1997, there is no general forecasting method, that performs best for all time series. Instead, expert knowledge is needed to decide which forecasting method to choose for a specific time series with its own characteristics. Since a trial and error approach is very inefficient and expert knowledge is useful but a time-consuming task that cannot be fully automated, we present a new hybrid multi-step-ahead forecasting approach based on time series decomposition. Initial evaluations show that this hybrid approach improves the forecast accuracy compared to six existing forecasting methods while maintaining a short runtime.}, added-at = {2020-04-06T11:24:19.000+0200}, author = {Z{\"u}fle, Marwin and Bauer, Andr{\'e} and Herbst, Nikolas and Curtef, Valentin and Kounev, Samuel}, biburl = {https://www.bibsonomy.org/bibtex/27e7149dd941b5e7f1dd9eafd25e23f0b/se-group}, booktitle = {{Proceedings of the International work-conference on Time Series (ITISE 2017)}}, interhash = {3b2232430c6a8415b88e989dd070c2a0}, intrahash = {7e7149dd941b5e7f1dd9eafd25e23f0b}, keywords = {Online_monitoring_and_forecasting Optimization Prediction Statistical_estimation_and_machine_learning Telescope Tool descartes t_extendedabstract}, month = {September}, timestamp = {2020-10-06T14:26:27.000+0200}, title = {{Telescope: A Hybrid Forecast Method for Univariate Time Series}}, year = 2017 }