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

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
Main Authors: Anastasios Kaltsounis, Evangelos Spiliotis, Vassilios Assimakopoulos
Format: Article
Language:English
Published: MDPI AG 2023-04-01
Series:Algorithms
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

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