In this chapter we summarize the main points of our overview and draw our conclusions. We discuss our interpretations about the reasons behind the different results and performance achieved by the Recurrent Neural Network architectures analyzed. We conclude by hypothesizing possible guidlines for selecting suitable models depending on the specific task at hand.

Conclusions / Bianchi, Filippo Maria; Maiorino, Enrico; Kampffmeyer, Michael C.; Rizzi, Antonello; Jenssen, Robert. - (2017), pp. 71-72. - SPRINGERBRIEFS IN COMPUTER SCIENCE. [10.1007/978-3-319-70338-1_8].

Conclusions

Bianchi, Filippo Maria;Maiorino, Enrico;Rizzi, Antonello;
2017

Abstract

In this chapter we summarize the main points of our overview and draw our conclusions. We discuss our interpretations about the reasons behind the different results and performance achieved by the Recurrent Neural Network architectures analyzed. We conclude by hypothesizing possible guidlines for selecting suitable models depending on the specific task at hand.
2017
Recurrent Neural Networks for Short-Term Load Forecasting. An Overview and Comparative Analysis
978-3-319-70337-4
978-3-319-70338-1
Deep learning; Recurrent neural networks; Short term load forecasting; Time series analysis; Computer Science (all)
02 Pubblicazione su volume::02a Capitolo o Articolo
Conclusions / Bianchi, Filippo Maria; Maiorino, Enrico; Kampffmeyer, Michael C.; Rizzi, Antonello; Jenssen, Robert. - (2017), pp. 71-72. - SPRINGERBRIEFS IN COMPUTER SCIENCE. [10.1007/978-3-319-70338-1_8].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1119435
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