An adaptive embedding procedure for time series forecasting with deep neural networks.

Succetti, Federico; Rosato, Antonello; Panella, Massimo

Succetti, Federico; Rosato, Antonello; Panella, Massimo.

Afiliación

Succetti F; Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome "La Sapienza", Via Eudossiana 18, 00184 Rome, Italy. Electronic address: federico.succetti@uniroma1.it.
Rosato A; Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome "La Sapienza", Via Eudossiana 18, 00184 Rome, Italy. Electronic address: antonello.rosato@uniroma1.it.
Panella M; Department of Information Engineering, Electronics and Telecommunications (DIET), University of Rome "La Sapienza", Via Eudossiana 18, 00184 Rome, Italy. Electronic address: massimo.panella@uniroma1.it.

Neural Netw ; 167: 715-729, 2023 Oct.

Article en En | MEDLINE | ID: mdl-37729787

RESUMEN

Nowadays, solving time series prediction problems is an open and challenging task. Many solutions are based on the implementation of deep neural architectures, which are able to analyze the structure of the time series and to carry out the prediction. In this work, we present a novel deep learning scheme based on an adaptive embedding mechanism. The latter is exploited to extract a compressed representation of the input time series that is used for the subsequent forecasting. The proposed model is based on a two-layer bidirectional Long Short-Term Memory network, where the first layer performs the adaptive embedding and the second layer acts as a predictor. The performances of the proposed forecasting scheme are compared with several models in two different scenarios, considering both well-known time series and real-life application cases. The experimental results show the accuracy and the flexibility of the proposed approach, which can be used as a prediction tool for any actual application.

Asunto(s)

Memoria a Largo Plazo; Redes Neurales de la Computación; Factores de Tiempo; Predicción

Palabras clave

Adaptive embedding; Deep neural network; Forecasting; Long Short-Term Memory; Time series

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Memoria a Largo Plazo Tipo de estudio: Prognostic_studies Idioma: En Revista: Neural Netw Asunto de la revista: NEUROLOGIA Año: 2023 Tipo del documento: Article Pais de publicación: Estados Unidos

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