Conditional Temporal Aggregation for Time Series Forecasting Using Feature-Based Meta-Learning

We present a machine learning approach for applying (multiple) temporal aggregation in time series forecasting settings. The method utilizes a classification model that can be used to either select the most appropriate temporal aggregation level for producing forecasts or to derive weights to proper...

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Bibliographic Details

Published in:	Algorithms
Main Authors:	Anastasios Kaltsounis, Evangelos Spiliotis, Vassilios Assimakopoulos
Format:	Article
Language:	English
Published:	MDPI AG 2023-04-01
Subjects:	multiple temporal aggregation time series forecasting time series features meta-learning forecasting accuracy
Online Access:	https://www.mdpi.com/1999-4893/16/4/206