nach oben

Erschienen in:

2024 | OriginalPaper | Buchkapitel

Kernel Representation Learning with Dynamic Regime Discovery for Time Series Forecasting

verfasst von : Kunpeng Xu, Lifei Chen, Jean-Marc Patenaude, Shengrui Wang

Erschienen in: Advances in Knowledge Discovery and Data Mining

Verlag: Springer Nature Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Correlations between variables in complex ecosystems such as weather and financial markets lead to a great amount of dynamic and co-evolving time series data, posing a significant challenge to the current forecast methods. Discovering dynamic patterns (aka regimes) is crucial to an accurate forecast, especially for the interpretability of the outcome. In this paper, we develop a kernel-based method to learn effective representations for capturing dynamically changing regimes. Each such representation accounts for the non-linear interactions among multiple time series, thereby facilitating more effective regime discovery. On the basis of regime information, we build a regression model to forecast all the variables simultaneously for the next multiple time points. The results on six real-life datasets demonstrate that our method can yield the most accurate forecast (with the lowest root mean square error) in comparison with seven predictive models.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel A Multi-view Feature Construction and Multi-Encoder-Decoder Transformer Architecture for Time Series Classification

Nächstes Kapitel Hyperparameter Tuning MLP’s for Probabilistic Time Series Forecasting

http://www.google.com/trends/.

https://archive.ics.uci.edu/ml/datasets/.

https://www.cs.ucr.edu/%7Eeamonn/time_series_data_2018.

Ardia, D., Bluteau, K., Boudt, K., Catania, L., Trottier, D.A.: Markov-switching garch models in r: the MSGARCH package. J. Stat. Softw. (2019)

Bazzi, M., Blasques, F., Koopman, S.J., Lucas, A.: Time-varying transition probabilities for Markov regime switching models. J. Time Series Anal. 38, 458–478 (2017)MathSciNetCrossRef

Box, G.: Box and jenkins: time series analysis, forecasting and control. In: A Very British Affair, pp. 161–215. Springer, Heidelberg (2013). https://doi.org/10.1057/9781137291264_6

Chatigny, P., Chen, R., Patenaude, J.M., Wang, S.: A variable-order regime switching model to identify significant patterns in financial markets. In: ICDM, pp. 887–892. IEEE (2018)

Chen, R., Paschalidis, I.: Selecting optimal decisions via distributionally robust nearest-neighbor regression. Adv. Neural Inf. Process. Syst. 32 (2019)

Dong, X., Dang, B., Zang, H., Li, S., Ma, D.: The prediction trend of enterprise financial risk based on machine learning arima model. J. Theory Pract. Eng. Sci. 4(01), 65–71 (2024)

Elhamifar, E., Vidal, R.: Sparse subspace clustering: algorithm, theory, and applications. IEEE TPAMI 35(11), 2765–2781 (2013)CrossRef

Hochstein, A., Ahn, H.I., Leung, Y.T., Denesuk, M.: Switching vector autoregressive models with higher-order regime dynamics application to prognostics and health management. In: PHM, pp. 1–10. IEEE (2014)

Lu, C., Feng, J., Lin, Z., Mei, T., Yan, S.: Subspace clustering by block diagonal representation. IEEE TPAMI 41(2), 487–501 (2018)CrossRef

10.

Matsubara, Y., Sakurai, Y.: Regime shifts in streams: real-time forecasting of co-evolving time sequences. In: ACM SIGKDD, pp. 1045–1054 (2016)

11.

Matsubara, Y., Sakurai, Y.: Dynamic modeling and forecasting of time-evolving data streams. In: ACM SIGKDD, pp. 458–468 (2019)

12.

Oreshkin, B.N., Carpov, D., Chapados, N., Bengio, Y.: N-beats: Neural basis expansion analysis for interpretable time series forecasting. arXiv preprint arXiv:1905.10437 (2019)

13.

Qiao, Y., Jin, J., Ni, F., Yu, J., Chen, W.: Application of machine learning in financial risk early warning and regional prevention and control: a systematic analysis based on shap. World Trends Realities Accompany. Prob. Dev. 331 (2023)

14.

Song, X., et al.: Zeroprompt: streaming acoustic encoders are zero-shot masked lms. arXiv preprint arXiv:2305.10649 (2023)

15.

Xu, C., Yu, J., Chen, W., Xiong, J.: Deep learning in photovoltaic power generation forecasting: Cnn-lstm hybrid neural network exploration and research. In: The 3rd International scientific and practical conference “Technologies in education in schools and universities”, Athens, Greece, 23–26 January 2024, vol. 363, p. 295. International Science Group (2024)

16.

Xu, K., Chen, L., Wang, S.: Data-driven kernel subspace clustering with local manifold preservation. In: 2022 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 876–884. IEEE (2022)

17.

Xu, K., Chen, L., Wang, S.: A multi-view kernel clustering framework for categorical sequences. Expert Syst. Appl. 116637 (2022)

18.

Xu, K., Chen, L., Wang, S., Wang, B.: A self-representation model for robust clustering of categorical sequences. In: U, L.H., Xie, H. (eds.) APWeb-WAIM 2018. LNCS, vol. 11268, pp. 13–23. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01298-4_2CrossRef

19.

Ye, J., et al.: Multiplexed oam beams classification via fourier optical convolutional neural network. In: 2023 IEEE Photonics Conference (IPC), pp. 1–2. IEEE (2023)

20.

Ye, J., Solyanik, M., Hu, Z., Dalir, H., Nouri, B.M., Sorger, V.J.: Free-space optical multiplexed orbital angular momentum beam identification system using fourier optical convolutional layer based on 4f system. In: Complex Light and Optical Forces XVII, vol. 12436, pp. 70–80. SPIE (2023)

21.

Zhou, H., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 11106–11115 (2021)

Titel: Kernel Representation Learning with Dynamic Regime Discovery for Time Series Forecasting
verfasst von: Kunpeng Xu
Lifei Chen
Jean-Marc Patenaude
Shengrui Wang
Verlag: Springer Nature Singapore
Buch: Advances in Knowledge Discovery and Data Mining
Print ISBN: 978-981-9722-65-5

Electronic ISBN: 978-981-9722-66-2

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-97-2266-2_20

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner