Forecasting Teleconsultation Demand with an Ensemble Attention-Based Bidirectional Long Short-Term Memory Model

Authors

Wenjia Chen¹, Lean Yu^{2, *,} , Jinlin Li^{1, *,}

¹School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China

²School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China

^*Corresponding author. Email: yulean@amss.ac.cn; jll_bit@163.com

DOI

10.2991/ijcis.d.210203.004How to use a DOI?

Keywords

Teleconsultation; Demand forecast; Holiday effect; Attention mechanism; Deep learning ensemble; Bidirectional long short-term memory (BILSTM)

Abstract

Accurate demand forecast can help improve teleconsultation efficiency. But teleconsultation demand forecast has not been reported in existing literature. For this purpose, the study proposes a novel model based on deep learning algorithm for daily teleconsultation demand forecast to fill in the research gap. Because of the significant effect of holidays on teleconsultation demand, holiday-related variables, and specific prediction technologies were selected to treat it. The technologies attention mechanism and bidirectional long short-term memory (BILSTM) were used to construct a novel forecasting methodology, i.e., ensemble attention-based BILSTM (EA-BILSTM), for the accurate forecasts. Based on actual teleconsultation data, the effectiveness of variable selection is verified by importing different inputs into models, and the superiority of EA-BILSTM is verified by comparison with nine benchmark models. Empirical results show that importing selected variables can lead to better forecasts and EA-BILSTM model can get lowest forecasting errors on two sub-datasets. This indicates that the proposed forecasting model is a high potential approach for teleconsultation demand prediction in the influence of sparse trait, like holiday effects.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Wenjia Chen
AU  - Lean Yu
AU  - Jinlin Li
PY  - 2021
DA  - 2021/02/10
TI  - Forecasting Teleconsultation Demand with an Ensemble Attention-Based Bidirectional Long Short-Term Memory Model
JO  - International Journal of Computational Intelligence Systems
SP  - 821
EP  - 833
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210203.004
DO  - 10.2991/ijcis.d.210203.004
ID  - Chen2021
ER  -