基于时间动态表示学习的实用技能需求预测
Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics
摘要：
快速的技术创新有可能将全球大部分劳动力抛在后面。当今的经济将对熟练劳动力的白热化需求与对数字经济毫无准备的工人的就业前景停滞不前相提并论。对每个国家来说，这都是一个危险和机遇并存的时刻，其结果将以长期资本配置和数十亿工人的生活满意度来衡量。为了迎接这一时刻，政府和市场必须找到方法，加快技能供应对需求变化的反应速度。更全面、更快速地了解劳动力市场情报是一条途径。在这项工作中，我们探讨了时间序列预测的效用，以提高从在线招聘广告收集的技能需求数据的价值。本文提出了一个管道，使一次性多步预测到未来使用十年的每月技能需求的观察的基础上，一组递归神经网络方法。我们比较了多变量模型与单变量模型的性能，分析了技能之间的相关性如何影响多变量模型的结果，并提出了对信息技术行业工人所实践的技能的需求预测。
英文摘要：
Rapid technological innovation threatens to leave much of the global workforce behind. Today's economy juxtaposes white-hot demand for skilled labor against stagnant employment prospects for workers unprepared to participate in a digital economy. It is a moment of peril and opportunity for every country, with outcomes measured in long-term capital allocation and the life satisfaction of billions of workers. To meet the moment, governments and markets must find ways to quicken the rate at which the supply of skills reacts to changes in demand. More fully and quickly understanding labor market intelligence is one route. In this work, we explore the utility of time series forecasts to enhance the value of skill demand data gathered from online job advertisements. This paper presents a pipeline which makes one-shot multi-step forecasts into the future using a decade of monthly skill demand observations based on a set of recurrent neural network methods. We compare the performance of a multivariate model versus a univariate one, analyze how correlation between skills can influence multivariate model results, and present predictions of demand for a selection of skills practiced by workers in the information technology industry.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
