Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Bücher
Zeitschriften
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
MTZ-Fachkongress

Antriebe und Energiesysteme von morgen


Austausch von Automobil- und Energiewirtschaft

Am 14./15. Mai geht es in Chemnitz um die Mobilitätswende durch Elektro- und Wasserstofffahrzeuge.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Buchtitelbild

2024 | OriginalPaper | Buchkapitel

FR\(^3\)LS: A Forecasting Model with Robust and Reduced Redundancy Latent Series

verfasst von : Abdallah Aaraba, Shengrui Wang, Jean-Marc Patenaude

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
loading …

Abstract

While some methods are confined to linear embeddings and others exhibit limited robustness, high-dimensional time series factorization techniques employ scalable matrix factorization for forecasting in latent space. This paper introduces a novel factorization method that employs a non-contrastive approach, guiding an autoencoder-like architecture to extract robust latent series while minimizing redundant information within the embeddings. The resulting learned representations are utilized by a temporal forecasting model, generating forecasts within the latent space, which are subsequently decoded back to the original space through the decoder. Extensive experiments demonstrate that our model achieves state-of-te-art performance on numerous commonly used datasets.

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
Nächstes Kapitel Knowledge-Infused Optimization for Parameter Selection in Numerical Simulations
Literatur
1.
Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:​1803.​01271 (2018)
2.
Bauwens, L., Laurent, S., Rombouts, J.V.: Multivariate garch models: a survey. J. Appl. Economet. 21(1), 79–109 (2006)MathSciNetCrossRef
3.
Bottou, L., Curtis, F.E., Nocedal, J.: Optimization methods for large-scale machine learning. SIAM Rev. 60(2), 223–311 (2018)MathSciNetCrossRef
4.
Cao, D., et al.: Spectral temporal graph neural network for multivariate time-series forecasting. Adv. Neural. Inf. Process. Syst. 33, 17766–17778 (2020)
5.
Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750–15758 (2021)
6.
Chung, J., Kastner, K., Dinh, L., Goel, K., Courville, A.C., Bengio, Y.: A recurrent latent variable model for sequential data. Adv. Neural Inf. Process. Syst. 28 (2015)
7.
Cuturi, M.: Fast global alignment kernels. In: Proceedings of the 28th International Conference on Machine Learning (ICML-2011), pp. 929–936 (2011)
8.
Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM. Neural Comput. 12(10), 2451–2471 (2000)CrossRef
9.
Gneiting, T., Raftery, A.E.: Strictly proper scoring rules, prediction, and estimation. J. Am. Stat. Assoc. 102(477), 359–378 (2007)MathSciNetCrossRef
10.
Grill, J.B., et al.: Bootstrap your own latent-a new approach to self-supervised learning. Adv. Neural. Inf. Process. Syst. 33, 21271–21284 (2020)
11.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)CrossRef
12.
Hyndman, R.J., Athanasopoulos, G.: Forecasting: principles and practice. OTexts (2018)
13.
14.
Kingma, D.P., Welling, M.: Stochastic gradient vb and the variational auto-encoder. In: Second International Conference on Learning Representations, ICLR, vol. 19, p. 121 (2014)
15.
Lai, G., Chang, W.C., Yang, Y., Liu, H.: Modeling long-and short-term temporal patterns with deep neural networks. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 95–104 (2018)
16.
Li, S., et al.: Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting. Adv. Neural Inf. Process. Syst. 32 (2019)
17.
18.
19.
Matheson, J.E., Winkler, R.L.: Scoring rules for continuous probability distributions. Manag. Sci. 22(10), 1087–1096 (1976)CrossRef
20.
McKenzie, E.: General exponential smoothing and the equivalent arma process. J. Forecast. 3(3), 333–344 (1984)CrossRef
21.
Mikolov, T., et al.: Statistical language models based on neural networks. In: Present. Google Mountain View, 2nd April 80(26) (2012)
22.
Nguyen, N., Quanz, B.: Temporal latent auto-encoder: a method for probabilistic multivariate time series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 9117–9125 (2021)
23.
Rangapuram, S.S., Seeger, M.W., Gasthaus, J., Stella, L., Wang, Y., Januschowski, T.: Deep state space models for time series forecasting. Adv. Neural Inf. Process. Syst. 31, 1–10 (2018)
24.
Rasul, K., Sheikh, A.S., Schuster, I., Bergmann, U., Vollgraf, R.: Multivariate probabilistic time series forecasting via conditioned normalizing flows. arXiv preprint arXiv:​2002.​06103 (2020)
25.
Salinas, D., Bohlke-Schneider, M., Callot, L., Medico, R., Gasthaus, J.: High-dimensional multivariate forecasting with low-rank gaussian copula processes. Adv. Neural Inf. Process. Syst. 32, 1–11 (2019)
26.
Salinas, D., Flunkert, V., Gasthaus, J., Januschowski, T.: DeepAR: probabilistic forecasting with autoregressive recurrent networks. Int. J. Forecast. 36(3), 1181–1191 (2020)CrossRef
27.
Sen, R., Yu, H.F., Dhillon, I.S.: Think globally, act locally: a deep neural network approach to high-dimensional time series forecasting. Adv. Neural Inf. Process. Syst. 32, 1–10 (2019)
28.
29.
30.
Trindade, A.: Electricityloaddiagrams20112014 data set. Center for Machine Learning and Intelligent Systems (2015)
31.
Wang, Y., Smola, A., Maddix, D., Gasthaus, J., Foster, D., Januschowski, T.: Deep factors for forecasting. In: International Conference on Machine Learning, pp. 6607–6617. PMLR (2019)
32.
Yu, H.F., Rao, N., Dhillon, I.S.: Temporal regularized matrix factorization for high-dimensional time series prediction. Adv. Neural Inf. Process. Syst. 29 (2016)
33.
Yue, Z., Wang, Y., Duan, J., Yang, T., Huang, C., Tong, Y., Xu, B.: Ts2vec: towards universal representation of time series. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 8980–8987 (2022)
34.
Zbontar, J., Jing, L., Misra, I., LeCun, Y., Deny, S.: Barlow twins: self-supervised learning via redundancy reduction. In: International Conference on Machine Learning, pp. 12310–12320. PMLR (2021)
35.
Zerveas, G., Jayaraman, S., Patel, D., Bhamidipaty, A., Eickhoff, C.: A transformer-based framework for multivariate time series representation learning. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, pp. 2114–2124 (2021)
36.
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)
Metadaten
Titel
FRLS: A Forecasting Model with Robust and Reduced Redundancy Latent Series
verfasst von
Abdallah Aaraba
Shengrui Wang
Jean-Marc Patenaude
Copyright-Jahr
2024
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_1

Premium Partner

    Bildnachweise