short-paper

An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series

Authors:

Johannes Grohmann,

Norbert Schmitt,

Nikolas Herbst,

Samuel KounevAuthors Info & Claims

ICPE '20: Proceedings of the ACM/SPEC International Conference on Performance Engineering

Pages 48 - 55

https://doi.org/10.1145/3358960.3379123

Published: 20 April 2020 Publication History

Abstract

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.

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Cited By

Koehler JBauer ADietz AKuenzer C(2022)Towards Forecasting Future Snow Cover Dynamics in the European Alps—The Potential of Long Optical Remote-Sensing Time SeriesRemote Sensing10.3390/rs1418446114:18(4461)Online publication date: 7-Sep-2022
https://doi.org/10.3390/rs14184461
Bauer AStraesser MBeierlieb LMeissner MKounev SFeng DBecker SHerbst NLeitner P(2022)Automated Triage of Performance Change Points Using Time Series Analysis and Machine LearningCompanion of the 2022 ACM/SPEC International Conference on Performance Engineering10.1145/3491204.3527486(29-32)Online publication date: 14-Jul-2022
https://dl.acm.org/doi/10.1145/3491204.3527486
Meisenbacher STurowski MPhipps KRätz MMüller DHagenmeyer VMikut R(2022)Review of automated time series forecasting pipelinesWIREs Data Mining and Knowledge Discovery10.1002/widm.147512:6Online publication date: 9-Aug-2022
https://doi.org/10.1002/widm.1475
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An Automated Forecasting Framework based on Method Recommendation for Seasonal Time Series

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Published In

cover image ACM Conferences

ICPE '20: Proceedings of the ACM/SPEC International Conference on Performance Engineering

April 2020

319 pages

ISBN:9781450369916

DOI:10.1145/3358960

General Chairs:
J. Nelson Amaral
University of Alberta, Canada
,
Anne Koziolek
Karlruhe Institute of Technology (KIT), Germany
,
Program Chairs:
Catia Trubiani
Gran Sasso Science Institute, GSSI, Italy
,
Alexandru Iosup
VU Amsterdam, Netherlands

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 20 April 2020

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Deutsche Forschungsgemeinschaft

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ICPE '20

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ICPE '20: ACM/SPEC International Conference on Performance Engineering

April 25 - 30, 2020

Edmonton AB, Canada

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ICPE '20 Paper Acceptance Rate 15 of 62 submissions, 24%;

Overall Acceptance Rate 252 of 851 submissions, 30%

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Cited By

Koehler JBauer ADietz AKuenzer C(2022)Towards Forecasting Future Snow Cover Dynamics in the European Alps—The Potential of Long Optical Remote-Sensing Time SeriesRemote Sensing10.3390/rs1418446114:18(4461)Online publication date: 7-Sep-2022
https://doi.org/10.3390/rs14184461
Bauer AStraesser MBeierlieb LMeissner MKounev SFeng DBecker SHerbst NLeitner P(2022)Automated Triage of Performance Change Points Using Time Series Analysis and Machine LearningCompanion of the 2022 ACM/SPEC International Conference on Performance Engineering10.1145/3491204.3527486(29-32)Online publication date: 14-Jul-2022
https://dl.acm.org/doi/10.1145/3491204.3527486
Meisenbacher STurowski MPhipps KRätz MMüller DHagenmeyer VMikut R(2022)Review of automated time series forecasting pipelinesWIREs Data Mining and Knowledge Discovery10.1002/widm.147512:6Online publication date: 9-Aug-2022
https://doi.org/10.1002/widm.1475
Odhiambo JWeke PNgare P(2021)A Deep Learning Integrated Cairns-Blake-Dowd (CBD) Sytematic Mortality Risk ModelJournal of Risk and Financial Management10.3390/jrfm1406025914:6(259)Online publication date: 8-Jun-2021
https://doi.org/10.3390/jrfm14060259
Weninger MGander EMössenböck H(2021)Guided ExplorationProceedings of the ACM on Human-Computer Interaction10.1145/34617315:EICS(1-34)Online publication date: 27-May-2021
https://dl.acm.org/doi/10.1145/3461731
Vaiciukynas EDanenas PKontrimas VButleris R(2021)Two-Step Meta-Learning for Time-Series Forecasting EnsembleIEEE Access10.1109/ACCESS.2021.30748919(62687-62696)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3074891
Grohmann JHerbst NChalbani AArian YPeretz NKounev S(2020)A Taxonomy of Techniques for SLO Failure Prediction in Software SystemsComputers10.3390/computers90100109:1(10)Online publication date: 11-Feb-2020
https://doi.org/10.3390/computers9010010

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