EMTSF:Extraordinary Mixture of SOTA Models for Time Series Forecasting
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866908614716817408 |
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| author | Alharthi, Musleh Mahmood, Kaleel Patel, Sarosh Mahmood, Ausif |
| author_facet | Alharthi, Musleh Mahmood, Kaleel Patel, Sarosh Mahmood, Ausif |
| contents | The immense success of the Transformer architecture
in Natural Language Processing has led to its adoption in Time Se ries Forecasting (TSF), where superior performance has been shown.
However, a recent important paper questioned their effectiveness by
demonstrating that a simple single layer linear model outperforms
Transformer-based models. This was soon shown to be not as valid,
by a better transformer-based model termed PatchTST. More re cently, TimeLLM demonstrated even better results by repurposing a
Large Language Model (LLM) for the TSF domain. Again, a follow
up paper challenged this by demonstrating that removing the LLM
component or replacing it with a basic attention layer in fact yields
better performance. One of the challenges in forecasting is the fact
that TSF data favors the more recent past, and is sometimes subject
to unpredictable events. Based upon these recent insights in TSF, we
propose a strong Mixture of Experts (MoE) framework. Our method
combines the state-of-the-art (SOTA) models including xLSTM, en hanced Linear, PatchTST, and minGRU, among others. This set of
complimentary and diverse models for TSF are integrated in a Trans former based MoE gating network. Our proposed model outperforms
all existing TSF models on standard benchmarks, surpassing even the
latest approaches based on MoE frameworks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_23396 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EMTSF:Extraordinary Mixture of SOTA Models for Time Series Forecasting Alharthi, Musleh Mahmood, Kaleel Patel, Sarosh Mahmood, Ausif Computation and Language Artificial Intelligence The immense success of the Transformer architecture in Natural Language Processing has led to its adoption in Time Se ries Forecasting (TSF), where superior performance has been shown. However, a recent important paper questioned their effectiveness by demonstrating that a simple single layer linear model outperforms Transformer-based models. This was soon shown to be not as valid, by a better transformer-based model termed PatchTST. More re cently, TimeLLM demonstrated even better results by repurposing a Large Language Model (LLM) for the TSF domain. Again, a follow up paper challenged this by demonstrating that removing the LLM component or replacing it with a basic attention layer in fact yields better performance. One of the challenges in forecasting is the fact that TSF data favors the more recent past, and is sometimes subject to unpredictable events. Based upon these recent insights in TSF, we propose a strong Mixture of Experts (MoE) framework. Our method combines the state-of-the-art (SOTA) models including xLSTM, en hanced Linear, PatchTST, and minGRU, among others. This set of complimentary and diverse models for TSF are integrated in a Trans former based MoE gating network. Our proposed model outperforms all existing TSF models on standard benchmarks, surpassing even the latest approaches based on MoE frameworks. |
| title | EMTSF:Extraordinary Mixture of SOTA Models for Time Series Forecasting |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2510.23396 |