Change Point Detection for Automatic Time Series Forecasting
DOI:
https://doi.org/10.47611/jsrhs.v13i2.6597Keywords:
Data Science, Forecasting, Time Series Analysis, Gaussian Processes, change point detection, manual kernel composition, fixed kernel compositionAbstract
Time series analysis, the process of learning patterns in time series data and making future predictions, is a challenging machine learning endeavor: the intricate and error-prone process of manual data fitting limits efficiency and scalability to larger datasets. Our research builds upon structure discovery research, paving the way for more effective short-term forecasting. We combine the adaptability of automatic change point detection with a fixed kernel composition, achieving accuracy comparable to traditional manual methods while reducing analysis time. This hybrid approach offers the best of both worlds: leveraging human expertise for precise fitting and capturing specific trends while utilizing an automated technique to recognize shifts and model complex relationships within the data dynamically. By demonstrating the effectiveness of automatic change point detection in conjunction with kernel composition, we work to develop time series usage in data analysis.
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References
Rob J. Hyndman and George Athanasopoulos. Forecasting: Principles and Practice. Otexts, Oct 2013.
ISBN 0987507109.
Can Wang, Mitra Baratchi, Thomas B ̈ack, Holger H. Hoos, Steffen Limmer, and Markus Olhofer.
Towards time-series feature engineering in automated machine learning for multi-step-ahead fore-
casting. In ITISE 2022, Basel Switzerland, Jun 2022. MDPI. URL http://dx.doi.org/10.3390/
engproc2022018017.
J. Scott Armstrong. Principles of forecasting: A handbook for researchers and practitioners,. 2001.
Giorgio Corani, Alessio Benavoli, and Marco Zaffalon. Time Series Forecasting with Gaussian Processes
Needs Priors, page 103–117. Springer International Publishing, 2021. ISBN 9783030865146. doi:
1007/978-3-030-86514-6 7. URL http://dx.doi.org/10.1007/978-3-030-86514-6_7.
Carl Edward. Rasmussen. Gaussian processes for machine learning. 2006.
Gerrit J. J. van den Burg and Christopher K. I. Williams. An evaluation of change point detection
algorithms, Mar 2020. URL https://arxiv.org/abs/2003.06222.
Kaggle competitions. https://www.kaggle.com/competitions.
Christopher K. I. Williams Carl Edward Rasmussen. Introduction. The MIT Press, 2005. URL http:
//dx.doi.org/10.7551/mitpress/3206.003.0004.
Samaneh Aminikhanghahi and Diane J. Cook. A survey of methods for time series change point detec-
tion. Knowledge and Information Systems, 51(2):339–367, Sep 2016. doi: 10.1007/s10115-016-0987-z.
Charles Truong; Laurent Oudre; Nicolas Vayatis;. Selective review of offline change point detection
methods. Mar 2020.
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. The m4 competition: 100,000
time series and 61 forecasting methods. International Journal of Forecasting, 36(1):54–74, 2020. ISSN
-2070. doi: https://doi.org/10.1016/j.ijforecast.2019.04.014. URL https://www.sciencedirect.
com/science/article/pii/S0169207019301128. M4 Competition.
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos. The m5 competition: Back-
ground, organization, and implementation. International Journal of Forecasting, 38(4):1325–1336,
ISSN 0169-2070. doi: https://doi.org/10.1016/j.ijforecast.2021.07.007. URL https://www.
sciencedirect.com/science/article/pii/S0169207021001187. Special Issue: M5 competition.
Jacob Gardner, Geoff Pleiss, Kilian Q Weinberger, David Bindel, and Andrew G Wilson. Gpytorch:
Blackbox matrix-matrix gaussian process inference with gpu acceleration. In S. Bengio, H. Wallach,
H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information
Processing Systems, volume 31. Curran Associates, Inc., 2018. URL https://proceedings.neurips.
cc/paper_files/paper/2018/file/27e8e17134dd7083b050476733207ea1-Paper.pdf.
Jonathan P. Chen, Neeraj Pradhan, and Zhen Cao. Autoforecasting library.
https://github.com/jpchen/autoforecaster.
David Duvenaud, James Robert Lloyd, Roger Grosse, Joshua B. Tenenbaum, and Zoubin Ghahramani.
Structure discovery in nonparametric regression through compositional kernel search, 2013.
Brian J. Patton Mansinghka, Feras A. Saad. Sequential monte carlo learning for time series structure
discovery. Jul 2023.
Marcel Bosc, Fabrice Heitz, Jean-Paul Armspach, Izzie Namer, Daniel Gounot, and Lucien Rumbach.
Automatic change detection in multimodal serial mri: application to multiple sclerosis lesion evolu-
tion. NeuroImage, 20(2):643–656, 2003. ISSN 1053-8119. doi: https://doi.org/10.1016/S1053-8119(03)
-3. URL https://www.sciencedirect.com/science/article/pii/S1053811903004063.
Jonas Dehning, Johannes Zierenberg, F. Paul Spitzner, Michael Wibral, Joao Pinheiro Neto, Michael
Wilczek, and Viola Priesemann. Inferring change points in the spread of covid-19 reveals the effectiveness
of interventions. Science, 369(6500):eabb9789, 2020. doi: 10.1126/science.abb9789. URL https://www.
science.org/doi/abs/10.1126/science.abb9789.
Veronika Smejkalov ́a, Radovan ˇSompl ́ak, Martin Roseck ́y, and Krist ́ına ˇSramkov ́a. Machine learn-
ing method for changepoint detection in short time series data. Machine Learning and Knowl-
edge Extraction, 5(4):1407–1432, 2023. ISSN 2504-4990. doi: 10.3390/make5040071. URL https:
//www.mdpi.com/2504-4990/5/4/71.
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