Unemployment Dynamics Forecasting with Machine Learning Regression Models (2505.01933v1)

Abstract: In this paper, I explored how a range of regression and machine learning techniques can be applied to monthly U.S. unemployment data to produce timely forecasts. I compared seven models: Linear Regression, SGDRegressor, Random Forest, XGBoost, CatBoost, Support Vector Regression, and an LSTM network, training each on a historical span of data and then evaluating on a later hold-out period. Input features include macro indicators (GDP growth, CPI), labor market measures (job openings, initial claims), financial variables (interest rates, equity indices), and consumer sentiment. I tuned model hyperparameters via cross-validation and assessed performance with standard error metrics and the ability to predict the correct unemployment direction. Across the board, tree-based ensembles (and CatBoost in particular) deliver noticeably better forecasts than simple linear approaches, while the LSTM captures underlying temporal patterns more effectively than other nonlinear methods. SVR and SGDRegressor yield modest gains over standard regression but don't match the consistency of the ensemble and deep-learning models. Interpretability tools ,feature importance rankings and SHAP values, point to job openings and consumer sentiment as the most influential predictors across all methods. By directly comparing linear, ensemble, and deep-learning approaches on the same dataset, our study shows how modern machine-learning techniques can enhance real-time unemployment forecasting, offering economists and policymakers richer insights into labor market trends. In the comparative evaluation of the models, I employed a dataset comprising thirty distinct features over the period from January 2020 through December 2024.

Insights on Unemployment Dynamics Forecasting with Machine-Learning Regression Models

This paper examines the efficacy of various regression and machine-learning models in forecasting U.S. unemployment rates using a dataset compiled from 30 macroeconomic indicators. It compares seven predictive methodologies: Linear Regression, SGDRegressor, Random Forest, XGBoost, CatBoost, Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) networks. The models utilize indicators spanning GDP growth, consumer sentiment, initial jobless claims, financial variables, and other key economic measures. The analysis aims to determine the most effective approach for capturing complex patterns within unemployment series to deliver timely and accurate forecasts.

Methodological Overview

The paper shines a light on the application of diverse machine-learning techniques and their comparative performance in forecasting unemployment dynamics. Key methodologies include:

• Linear Models: Represented by traditional linear regression and incrementally advanced SGDRegressor, which are foundational but limited in capturing non-linear relationships.
• Tree-Based Ensembles: Consisting of Random Forest, XGBoost, and CatBoost, these models exploit interactions among numerous predictors to provide superior forecast accuracy. Notably, CatBoost emerges as a dominant model in ensuring reduced prediction bias in using categorical splits.
• SVR: Despite improving upon pure linear approaches, SVR lags behind ensemble models due to its computational intensity.
• LSTM: This deep-learning method excels in identifying temporal dependencies in data. However, in this paper, the LSTM's performance was less advantageous due to the smooth and slowly changing nature of unemployment data.

Data preprocessing involved evaluating various scaling methods, with MaxAbsScaler identified as the most conducive to model accuracy across algorithms by preserving feature range and mitigating outlier influence effectively.

Results and Analysis

The comparative analysis of the models was rooted in their performance measured by Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE). The Random Forest model emerged as the most effective, consistently delivering the lowest forecasting errors. This aligns with the model's inherent capability to navigate intricate non-linear interactions, achieving robust performance without requiring extensive feature engineering or parameter tuning.

Moreover, the CatBoost and XGBoost also provided reliable predictions, though did not surpass Random Forest in statistical metrics. On the contrary, conventional linear methods and SVR demonstrated significant limitations, unable to compete with ensemble approaches.

Implications and Future Directions

The paper’s outcomes have both practical and theoretical implications for labor market analysis. Practically, the findings suggest tree-based models, particularly Random Forest, as optimal candidates for unemployment forecasting tasks. This can aid policymakers and economists in deriving insights relevant to timely interventions and strategic planning in labor markets.

On a theoretical level, the results encourage further investigation into hybrid modeling approaches, combining the strengths of ensemble and deep-learning techniques, especially in contexts of dynamic economic periods or for other macroeconomic indicators. Additionally, exploring the inclusion of high-frequency or real-time data and leveraging rolling-window cross-validation could refine model resilience across varied economic conditions.

Overall, this research contributes a substantive evaluation of machine-learning models for economic forecasting, setting a foundation for more nuanced and computationally efficient future models in macroeconomic domains.