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Patch-Based Transformers for Long-Term Energy Consumption Forecasting
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DOI：10.1142/S0218213025400135.png)
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Abstract
En 中文
Patch-based Transformer models have gained widespread adoption, achieving state-of-the-art performance across various domains that involve multi-dimensional spatio-temporal data, such as, for example, in vision tasks. Recently, they have emerged as a promising alternative for multivariate time-series forecasting, where each univariate series is treated as a separate channel, while sharing the same embedding and Transformer weights. In this work, we further explore the capabilities of patch-based Transformers in the context of forecasting a single time series, specifically focusing on energy consumption prediction. Our primary interest lies in long-term forecasting, a relatively under-explored area in the literature. To this end, we evaluate Transformer-based models on two energy consumption datasets - one public and one private - and assess their performance. We argue that leveraging patches or patching-like techniques can significantly enhance model efficiency. Lastly, we discuss the current limitations of Transformer-based architectures and propose potential solutions.
Keywords:
Patch-based transformers
long-term time series forecasting
energy consumption
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Cited Papers
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