Test-Time Efficient Pretrained Model Portfolios for Time Series Forecasting
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866910019567484928 |
|---|---|
| author | Kayaalp, Mert Turkmen, Caner Shchur, Oleksandr Mercado, Pedro Ansari, Abdul Fatir Bohlke-Schneider, Michael Wang, Bernie |
| author_facet | Kayaalp, Mert Turkmen, Caner Shchur, Oleksandr Mercado, Pedro Ansari, Abdul Fatir Bohlke-Schneider, Michael Wang, Bernie |
| contents | Is bigger always better for time series foundation models? With the question in mind, we explore an alternative to training a single, large monolithic model: building a portfolio of smaller, pretrained forecasting models. By applying ensembling or model selection over these portfolios, we achieve competitive performance on large-scale benchmarks using much fewer parameters. We explore strategies for designing such portfolios and find that collections of specialist models consistently outperform portfolios of independently trained generalists. Remarkably, we demonstrate that post-training a base model is a compute-effective approach for creating sufficiently diverse specialists, and provide evidences that ensembling and model selection are more compute-efficient than test-time fine-tuning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_06419 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Test-Time Efficient Pretrained Model Portfolios for Time Series Forecasting Kayaalp, Mert Turkmen, Caner Shchur, Oleksandr Mercado, Pedro Ansari, Abdul Fatir Bohlke-Schneider, Michael Wang, Bernie Machine Learning Is bigger always better for time series foundation models? With the question in mind, we explore an alternative to training a single, large monolithic model: building a portfolio of smaller, pretrained forecasting models. By applying ensembling or model selection over these portfolios, we achieve competitive performance on large-scale benchmarks using much fewer parameters. We explore strategies for designing such portfolios and find that collections of specialist models consistently outperform portfolios of independently trained generalists. Remarkably, we demonstrate that post-training a base model is a compute-effective approach for creating sufficiently diverse specialists, and provide evidences that ensembling and model selection are more compute-efficient than test-time fine-tuning. |
| title | Test-Time Efficient Pretrained Model Portfolios for Time Series Forecasting |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.06419 |