What is the average drop in forecasting accuracy for Llama3 compared to domain-specific models when evaluated

Abstract

Abstract: Time series forecasting remains a challenging task, particularly in the context of complex multiscale temporal patterns. This study presents LLM-Mixer, a framework that improves forecasting accuracy through the combination of multiscale time-series decomposition with pre-trained LLMs (Large Language Models). LLM-Mixer captures both short-term fluctuations and long-term trends by decomposing the data into multiple temporal resolutions and processing them with a frozen LLM, guided by a textual prompt specifically designed for time-series data. Extensive experiments conducted on multivariate and

Research Question

What is the average drop in forecasting accuracy for Llama3 compared to domain-specific models when evaluated on renewable energy time-series data?

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