Scaling Transformers for Time Series Forecasting: Do Pretrained Large Models Outperform Small-Scale Alternatives?

• URL: http://arxiv.org/abs/2507.02907v1
• Date: Tue, 24 Jun 2025 11:54:10 GMT
• Title: Scaling Transformers for Time Series Forecasting: Do Pretrained Large Models Outperform Small-Scale Alternatives?
• Authors: Sanjay Chakraborty, Ibrahim Delibasoglu, Fredrik Heintz,
• Abstract summary: This work examines whether pre-trained large-scale time series models (LSTSMs) can outperform traditional non-pretrained small-scale transformers in forecasting tasks.<n>We analyze state-of-the-art (SOTA) pre-trained universal time series models (e.g., Moirai, TimeGPT) alongside conventional transformers.<n>Our findings reveal the strengths and limitations of pre-trained LSTSMs, providing insights into their suitability for time series tasks.
• Score: 4.075971633195745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large pre-trained models have demonstrated remarkable capabilities across domains, but their effectiveness in time series forecasting remains understudied. This work empirically examines whether pre-trained large-scale time series models (LSTSMs) trained on diverse datasets can outperform traditional non-pretrained small-scale transformers in forecasting tasks. We analyze state-of-the-art (SOTA) pre-trained universal time series models (e.g., Moirai, TimeGPT) alongside conventional transformers, evaluating accuracy, computational efficiency, and interpretability across multiple benchmarks. Our findings reveal the strengths and limitations of pre-trained LSTSMs, providing insights into their suitability for time series tasks compared to task-specific small-scale architectures. The results highlight scenarios where pretraining offers advantages and where simpler models remain competitive.

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