Pay Attention to Evolution : Time Series Forecasting with Deep Graph-Evolution Learning

Time-series forecasting is one of the most active research topics in artificial intelligence. A still open gap in that literature is that statistical and ensemble learning approaches systematically present lower predictive performance than deep learning methods. They generally disregard the data sequence aspect entangled with multivariate data represented in more than one time series. Conversely, this work presents a novel neural network architecture for time-series forecasting that combines the power of graph evolution with deep recurrent learning on distinct data distributions; we named our method Recurrent Graph Evolution Neural Network (ReGENN). The idea is to infer multiple multivariate relationships between co-occurring time-series by assuming that the temporal data depends not only on inner variables and intra-temporal relationships (i.e., observations from itself) but also on outer variables and inter-temporal relationships (i.e., observations from other-selves). An extensive set of experiments was conducted comparing ReGENN with dozens of ensemble methods and classical statistical ones, showing sound improvement of up to 64.87% over the competing algorithms. Furthermore, we present an analysis of the intermediate weights arising from ReGENN, showing that by looking at inter and intra-temporal relationships simultaneously, time-series forecasting is majorly improved if paying attention to how multiple multivariate data synchronously evolve.

Medienart:

E-Artikel

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

Zur Gesamtaufnahme - volume:PP

Enthalten in:

IEEE transactions on pattern analysis and machine intelligence - PP(2021) vom: 27. Apr.

Sprache:

Englisch

Beteiligte Personen:

Spadon, Gabriel [VerfasserIn]
Hong, Shenda [VerfasserIn]
Brandoli, Bruno [VerfasserIn]
Matwin, Stan [VerfasserIn]
Rodrigues-Jr, Jose Fernando [VerfasserIn]
Sun, Jimeng [VerfasserIn]

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Volltext

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Journal Article

Anmerkungen:

Date Revised 22.02.2024

published: Print-Electronic

Citation Status Publisher

doi:

10.1109/TPAMI.2021.3076155

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM324587392