Deep LSTM-Based Transfer Learning Approach for Coherent Forecasts in Hierarchical Time Series

Deep LSTM-Based Transfer Learning Approach for Coherent Forecasts in Hierarchical Time Series

Hierarchical time series is a set of data sequences organized by aggregation constraints to represent many real-world applications in research and the industry. Forecasting of hierarchical time series is a challenging and time-consuming problem owing to ensuring the forecasting consistency among the...

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Bibliographic Details
Main Authors:	Alaa Sagheer, Hala Hamdoun, Hassan Youness
Format:	Article
Language:	English
Published:	MDPI AG 2021-06-01
Series:	Sensors
Subjects:	deep long short-term memory auto-encoder hierarchical time series coherent forecast power generation australian tourism
Online Access:	https://www.mdpi.com/1424-8220/21/13/4379

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