Forecasting Labor Demand: Predicting JOLT Job Openings using Deep Learning Model

• URL: http://arxiv.org/abs/2503.19048v1
• Date: Mon, 24 Mar 2025 18:19:33 GMT
• Title: Forecasting Labor Demand: Predicting JOLT Job Openings using Deep Learning Model
• Authors: Kyungsu Kim,
• Abstract summary: This thesis studies the effectiveness of Long Short Term Memory model in forecasting future Job openings and Labor Turnover Survey data in the United States.<n>Findings suggest that the LSTM model outperforms conventional autoregressive approaches, including ARIMA, SARIMA, and Holt-Winters.<n>These results highlight the potential of deep learning techniques in capturing complex temporal dependencies in economic data, offering valuable insights for policymakers and stakeholders in developing data-driven labor market strategies.
• Score: 1.9761774213809031
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This thesis studies the effectiveness of Long Short Term Memory model in forecasting future Job Openings and Labor Turnover Survey data in the United States. Drawing on multiple economic indicators from various sources, the data are fed directly into LSTM model to predict JOLT job openings in subsequent periods. The performance of the LSTM model is compared with conventional autoregressive approaches, including ARIMA, SARIMA, and Holt-Winters. Findings suggest that the LSTM model outperforms these traditional models in predicting JOLT job openings, as it not only captures the dependent variables trends but also harmonized with key economic factors. These results highlight the potential of deep learning techniques in capturing complex temporal dependencies in economic data, offering valuable insights for policymakers and stakeholders in developing data-driven labor market strategies

Related papers

• Efficient Model Selection for Time Series Forecasting via LLMs [52.31535714387368]
  We propose to leverage Large Language Models (LLMs) as a lightweight alternative for model selection. Our method eliminates the need for explicit performance matrices by utilizing the inherent knowledge and reasoning capabilities of LLMs.
  arXiv  Detail & Related papers  (2025-04-02T20:33:27Z)
• A Dynamic and High-Precision Method for Scenario-Based HRA Synthetic Data Collection in Multi-Agent Collaborative Environments Driven by LLMs [2.50572897318757]
  This study introduces a novel, scenario-driven method for workload estimation, leveraging fine-tuned large language models (LLMs)<n>The method dynamically adapts to changes in operator workload, providing more accurate, flexible, and scalable workload estimates.
  arXiv  Detail & Related papers  (2025-01-16T09:23:48Z)
• HMM-LSTM Fusion Model for Economic Forecasting [0.0]
  This paper explores the application of Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) neural networks for economic forecasting.<n>The study explores a new approach that integrates HMM-derived hidden states and means as additional features for LSTM modeling.
  arXiv  Detail & Related papers  (2025-01-01T17:31:36Z)
• Optimizing Sequential Recommendation Models with Scaling Laws and Approximate Entropy [104.48511402784763]
  Performance Law for SR models aims to theoretically investigate and model the relationship between model performance and data quality.<n>We propose Approximate Entropy (ApEn) to assess data quality, presenting a more nuanced approach compared to traditional data quantity metrics.
  arXiv  Detail & Related papers  (2024-11-30T10:56:30Z)
• Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Exploration [90.41908331897639]
  Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data. We present a novel approach, ReverseGen, designed to automatically generate effective training samples.
  arXiv  Detail & Related papers  (2024-10-22T06:43:28Z)
• On conditional diffusion models for PDE simulations [53.01911265639582]
  We study score-based diffusion models for forecasting and assimilation of sparse observations. We propose an autoregressive sampling approach that significantly improves performance in forecasting. We also propose a new training strategy for conditional score-based models that achieves stable performance over a range of history lengths.
  arXiv  Detail & Related papers  (2024-10-21T18:31:04Z)
• MITA: Bridging the Gap between Model and Data for Test-time Adaptation [68.62509948690698]
  Test-Time Adaptation (TTA) has emerged as a promising paradigm for enhancing the generalizability of models. We propose Meet-In-The-Middle based MITA, which introduces energy-based optimization to encourage mutual adaptation of the model and data from opposing directions.
  arXiv  Detail & Related papers  (2024-10-12T07:02:33Z)
• Stock Price Prediction and Traditional Models: An Approach to Achieve Short-, Medium- and Long-Term Goals [0.0]
  A comparative analysis of deep learning models and traditional statistical methods for stock price prediction uses data from the Nigerian stock exchange. Deep learning models, particularly LSTM, outperform traditional methods by capturing complex, nonlinear patterns in the data. The findings highlight the potential of deep learning for improving financial forecasting and investment strategies.
  arXiv  Detail & Related papers  (2024-09-29T11:20:20Z)
• LABOR-LLM: Language-Based Occupational Representations with Large Language Models [8.909328013944567]
  CAREER predicted a worker's next job as a function of career history.<n>This paper considers an alternative where the resume-based foundation model is replaced by a large language model.
  arXiv  Detail & Related papers  (2024-06-25T23:07:18Z)
• F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data [65.6499834212641]
  We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm. By considering domain similarities through task-specific metadata, our model improved generalization, where the excess risk decreases as the number of training tasks increases. Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
  arXiv  Detail & Related papers  (2024-06-23T21:28:50Z)
• CALF: Aligning LLMs for Time Series Forecasting via Cross-modal Fine-Tuning [59.88924847995279]
  We propose a novel Cross-Modal LLM Fine-Tuning (CALF) framework for MTSF. To reduce the distribution discrepancy, we develop the cross-modal match module. CALF establishes state-of-the-art performance for both long-term and short-term forecasting tasks.
  arXiv  Detail & Related papers  (2024-03-12T04:04:38Z)
• A Deep Learning Framework for Predicting Digital Asset Price Movement from Trade-by-trade Data [20.392440676633573]
  This paper presents a framework that predicts price movement of cryptocurrencies from trade-by-trade data. The model is trained to achieve high performance on nearly a year of trade-by-trade data. In a realistic trading simulation setting, the prediction made by the model could be easily monetized.
  arXiv  Detail & Related papers  (2020-10-11T10:42:02Z)
• Deep Stock Predictions [58.720142291102135]
  We consider the design of a trading strategy that performs portfolio optimization using Long Short Term Memory (LSTM) neural networks. We then customize the loss function used to train the LSTM to increase the profit earned. We find the LSTM model with the customized loss function to have an improved performance in the training bot over a regressive baseline such as ARIMA.
  arXiv  Detail & Related papers  (2020-06-08T23:37:47Z)
• A Time Series Analysis-Based Stock Price Prediction Using Machine Learning and Deep Learning Models [0.0]
  We present a very robust and accurate framework of stock price prediction that consists of an agglomeration of statistical, machine learning and deep learning models. We use the daily stock price data, collected at five minutes interval of time, of a very well known company that is listed in the National Stock Exchange (NSE) of India. We contend that the agglomerative approach of model building that uses a combination of statistical, machine learning, and deep learning approaches, can very effectively learn from the volatile and random movement patterns in a stock price data.
  arXiv  Detail & Related papers  (2020-04-17T19:41:22Z)

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.