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LOTUS：通过学习预测来理解时间序列Lotus: Learning to Understand Time-series by Learning to Forecast
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以下内容为 AI 自动翻译，英文原文附后供参考。
摘要
研究人员推出了LOTUS，这是一种新型的时间序列语言模型，旨在提高预测准确性。该模型采用了一种独特的预训练原则，称为预测基础，能够将时间序列数据分割成可管理的块。通过利用数据的原始和频谱表示，LOTUS不仅增强了预测能力，而且与现有模型相比，参数规模更小。LOTUS的推出标志着时间序列数据处理和理解方式的重大进步，可能会在各个行业中带来更高效的应用。
Researchers have unveiled LOTUS, a novel time-series language model designed to improve forecasting accuracy. This model employs a unique pretraining principle known as forecast grounding, which segments time-series data into manageable patches. By leveraging both raw and spectral representations of the data, LOTUS not only enhances predictive capabilities but also achieves this with a smaller parameter size compared to existing models in the field. The introduction of LOTUS marks a significant advancement in the way time-series data is processed and understood, potentially leading to more efficient applications in various industries.
要点
LOTUS引入了一种新的预训练原则，称为预测基础。
LOTUS introduces a new pretraining principle called forecast grounding.
该模型将时间序列数据分割成块以便更好地处理。
The model segments time-series data into patches for better processing.
LOTUS在参数更少的情况下实现了比现有模型更高的准确性。
LOTUS achieves higher accuracy with fewer parameters than existing models.
为何重要
LOTUS的发展代表了时间序列分析的重要进展，这对从金融到医疗等众多应用至关重要。改进的预测模型可以在各个领域带来更好的决策和资源分配。
The development of LOTUS represents a significant step forward in time-series analysis, which is crucial for numerous applications ranging from finance to healthcare. Improved forecasting models can lead to better decision-making and resource allocation in various sectors.
产品经理
对于产品经理来说，LOTUS提供了利用先进预测能力的机会，可能增强依赖时间序列数据的产品特性，如需求预测和趋势分析。
For product managers, LOTUS provides an opportunity to leverage advanced forecasting capabilities, potentially enhancing product features that rely on time-series data, such as demand forecasting and trend analysis.
开发者
开发人员和工程师可以利用LOTUS构建更高效的需要时间序列分析的应用，受益于其较小的参数规模，同时保持高预测准确性。
Developers and engineers can utilize LOTUS to build more efficient applications that require time-series analysis, benefiting from its reduced parameter size while maintaining high accuracy in predictions.
学生与求职者
学生和求职者应关注LOTUS，因为它突显了时间序列分析在数据科学和机器学习中的日益重要性，提供了开发和应用此类模型的潜在职业机会。
Students and job-seekers should take note of LOTUS as it highlights the growing importance of time-series analysis in data science and machine learning, presenting potential career opportunities in developing and applying such models.
覆盖来源
来源标题与摘录保留原文，以便核对与追溯。
arXiv cs.LG
Lotus: Learning to Understand Time-series by Learning to ForecastResearchers introduced LOTUS, a time-series language model that enhances predictive capabilities through a pretraining principle called forecast grounding. By segmenting time-series data into patches and utilizing both raw and spectral representations, LOTUS achieves superior accuracy with a reduced parameter size compared to existing models.