Hierarchical Time Series Forecasting Via Latent Mean Encoding
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913910333898752 |
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| author | Salatiello, Alessandro Birr, Stefan Kunz, Manuel |
| author_facet | Salatiello, Alessandro Birr, Stefan Kunz, Manuel |
| contents | Coherently forecasting the behaviour of a target variable across both coarse and fine temporal scales is crucial for profit-optimized decision-making in several business applications, and remains an open research problem in temporal hierarchical forecasting. Here, we propose a new hierarchical architecture that tackles this problem by leveraging modules that specialize in forecasting the different temporal aggregation levels of interest. The architecture, which learns to encode the average behaviour of the target variable within its hidden layers, makes accurate and coherent forecasts across the target temporal hierarchies. We validate our architecture on the challenging, real-world M5 dataset and show that it outperforms established methods, such as the TSMixer model. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_19633 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hierarchical Time Series Forecasting Via Latent Mean Encoding Salatiello, Alessandro Birr, Stefan Kunz, Manuel Machine Learning Artificial Intelligence Coherently forecasting the behaviour of a target variable across both coarse and fine temporal scales is crucial for profit-optimized decision-making in several business applications, and remains an open research problem in temporal hierarchical forecasting. Here, we propose a new hierarchical architecture that tackles this problem by leveraging modules that specialize in forecasting the different temporal aggregation levels of interest. The architecture, which learns to encode the average behaviour of the target variable within its hidden layers, makes accurate and coherent forecasts across the target temporal hierarchies. We validate our architecture on the challenging, real-world M5 dataset and show that it outperforms established methods, such as the TSMixer model. |
| title | Hierarchical Time Series Forecasting Via Latent Mean Encoding |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2506.19633 |