Practical Skills Demand Forecasting via Representation Learning of
Temporal Dynamics
- URL: http://arxiv.org/abs/2205.09508v1
- Date: Wed, 18 May 2022 04:02:55 GMT
- Title: Practical Skills Demand Forecasting via Representation Learning of
Temporal Dynamics
- Authors: Maysa M. Garcia de Macedo and Wyatt Clarke and Eli Lucherini and Tyler
Baldwin and Dilermando Queiroz Neto and Rogerio de Paula and Subhro Das
- Abstract summary: Rapid technological innovation threatens to leave much of the global workforce behind.
Governments and markets must find ways to quicken the rate at which the supply of skills reacts to changes in demand.
This paper presents a pipeline which makes one-shot multi-step forecasts into the future using a decade of monthly skill demand observations.
- Score: 4.536775100566484
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Latent-Predictive Empowerment: Measuring Empowerment without a Simulator [56.53777237504011]
We present Latent-Predictive Empowerment (LPE), an algorithm that can compute empowerment in a more practical manner.
LPE learns large skillsets by maximizing an objective that is a principled replacement for the mutual information between skills and states.
arXiv Detail & Related papers (2024-10-15T00:41:18Z) - Comparative Analysis of Encoder-Based NER and Large Language Models for Skill Extraction from Russian Job Vacancies [0.0]
This study compares Named Entity Recognition methods based on encoders with Large Language Models (LLMs) for extracting skills from Russian job vacancies.
Results indicate that traditional NER models, especially DeepPavlov RuBERT NER tuned, outperform LLMs across various metrics including accuracy, precision, recall, and inference time.
This research contributes to the field of natural language processing (NLP) and its application in the labor market, particularly in non-English contexts.
arXiv Detail & Related papers (2024-07-29T09:08:40Z) - Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking [59.87055275344965]
Job-SDF is a dataset designed to train and benchmark job-skill demand forecasting models.
Based on 10.35 million public job advertisements collected from major online recruitment platforms in China between 2021 and 2023.
Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels.
arXiv Detail & Related papers (2024-06-17T07:22:51Z) - Multi-generational labour markets: data-driven discovery of
multi-perspective system parameters using machine learning [0.36832029288386137]
We use big data and machine learning methods to discover multi-perspective parameters for multi-generational labour markets.
The parameters for the academic perspective are discovered using 35,000 article abstracts from the Web of Science for the period 1958-2022.
A complete machine learning software tool is developed for data-driven parameter discovery.
arXiv Detail & Related papers (2023-02-20T18:25:10Z) - A practical method for occupational skills detection in Vietnamese job
listings [0.16114012813668932]
Lack of accurate and timely labor market information leads to skill miss-matches.
Traditional approaches rely on existing taxonomy and/or large annotated data.
We propose a practical methodology for skill detection in Vietnamese job listings.
arXiv Detail & Related papers (2022-10-26T10:23:18Z) - Forecasting Future World Events with Neural Networks [68.43460909545063]
Autocast is a dataset containing thousands of forecasting questions and an accompanying news corpus.
The news corpus is organized by date, allowing us to precisely simulate the conditions under which humans made past forecasts.
We test language models on our forecasting task and find that performance is far below a human expert baseline.
arXiv Detail & Related papers (2022-06-30T17:59:14Z) - What Should I Know? Using Meta-gradient Descent for Predictive Feature
Discovery in a Single Stream of Experience [63.75363908696257]
computational reinforcement learning seeks to construct an agent's perception of the world through predictions of future sensations.
An open challenge in this line of work is determining from the infinitely many predictions that the agent could possibly make which predictions might best support decision-making.
We introduce a meta-gradient descent process by which an agent learns what predictions to make, 2) the estimates for its chosen predictions, and 3) how to use those estimates to generate policies that maximize future reward.
arXiv Detail & Related papers (2022-06-13T21:31:06Z) - Finding General Equilibria in Many-Agent Economic Simulations Using Deep
Reinforcement Learning [72.23843557783533]
We show that deep reinforcement learning can discover stable solutions that are epsilon-Nash equilibria for a meta-game over agent types.
Our approach is more flexible and does not need unrealistic assumptions, e.g., market clearing.
We demonstrate our approach in real-business-cycle models, a representative family of DGE models, with 100 worker-consumers, 10 firms, and a government who taxes and redistributes.
arXiv Detail & Related papers (2022-01-03T17:00:17Z) - Low-skilled Occupations Face the Highest Upskilling Pressure [0.0]
We examine how job contents evolve as new technologies substitute for tasks, shifting required skills rather than eliminating entire jobs.
We find that re-skilling pressure is greatest for low-skilled occupations when accounting for distance between skills.
Jobs from large employers and markets experienced less change relative to small employers and markets, and non-white workers in low-skilled jobs are most demographically vulnerable.
arXiv Detail & Related papers (2021-01-27T16:02:57Z) - Knowledge-driven Data Construction for Zero-shot Evaluation in
Commonsense Question Answering [80.60605604261416]
We propose a novel neuro-symbolic framework for zero-shot question answering across commonsense tasks.
We vary the set of language models, training regimes, knowledge sources, and data generation strategies, and measure their impact across tasks.
We show that, while an individual knowledge graph is better suited for specific tasks, a global knowledge graph brings consistent gains across different tasks.
arXiv Detail & Related papers (2020-11-07T22:52:21Z) - Predicting Skill Shortages in Labor Markets: A Machine Learning Approach [7.503338065129185]
This research implements a high-performing Machine Learning approach to predict occupational skill shortages.
We compile a unique dataset of both Labor Demand and Labor Supply occupational data in Australia from 2012 to 2018.
Job ads data and employment statistics were the highest performing feature sets for predicting year-to-year skills shortage changes for occupations.
arXiv Detail & Related papers (2020-04-03T00:15:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.