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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https://www.mdpi.com/1424-8220/21/13/4379

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