Machine Learning Advances for Time Series Forecasting (2012.12802v3)

Abstract: In this paper we survey the most recent advances in supervised machine learning and high-dimensional models for time series forecasting. We consider both linear and nonlinear alternatives. Among the linear methods we pay special attention to penalized regressions and ensemble of models. The nonlinear methods considered in the paper include shallow and deep neural networks, in their feed-forward and recurrent versions, and tree-based methods, such as random forests and boosted trees. We also consider ensemble and hybrid models by combining ingredients from different alternatives. Tests for superior predictive ability are briefly reviewed. Finally, we discuss application of machine learning in economics and finance and provide an illustration with high-frequency financial data.

• The paper demonstrates that penalized linear regressions and advanced estimators improve forecasting accuracy on high-dimensional datasets.
• It details the application of nonlinear models, including neural networks and tree-based methods, to capture complex time series patterns.
• By validating ensemble and hybrid approaches on financial data, the study highlights the practical impact of ML in economic forecasting.

Machine Learning Advances for Time Series Forecasting

This comprehensive paper explores advances in ML methods for time series forecasting, focusing on both linear and nonlinear approaches. It addresses applications within economics and finance, detailing innovations in supervised learning models to enhance predictive accuracy in settings enriched with high-dimensional data. The paper offers insights into several sophisticated ML methods and demonstrates their deployment within economic and financial contexts, emphasizing machine learning's growing impact on predictive modeling.

Key Highlights

1. Linear Models and Penalized Regressions: The research gives significant attention to linear techniques, primarily focusing on penalized regressions, including Ridge Regression, LASSO, and its variants. The paper discusses regularization, which provides a mechanism to manage overfitting, especially crucial when dealing with high-dimensional data. Amidst various estimators, methodologies like AdaLASSO and Elastic Net are highlighted for their robust variable selection capabilities and enhanced prediction consistency.
2. Nonlinear Techniques: The paper elaborates on the nonlinear frameworks such as neural networks (NN) and tree-based models. The versatility of these models in handling complex datasets is underscored. The exploration includes shallow and deep neural networks, revealing the hierarchical model structures capable of capturing intricate data patterns. Furthermore, it presents recurrent neural networks (RNN) as models capable of efficiently handling sequential dependencies inherent in time series.
3. Ensemble and Hybrid Methods: The paper explores ensemble methods like Bagging and Complete Subset Regression (CSR), which aggregate information from multiple models to enhance predictive accuracy. Hybrid approaches, which integrate properties of both linear and nonlinear models, are discussed as promising avenues for capturing diverse data regularities.
4. Empirical Illustration: To ground these theoretical advancements, an empirical application to high-frequency financial data is provided. The models demonstrated include AdaLASSO-enhanced HAR models, Random Forests, and neural networks illustrating significant improvements in forecasting realized volatility of financial indices in Brazil. This application serves not only as a validation of the discussed models but also as an indication of their potential in economic scenarios characterized by volatility or high-dimensional datasets.

Implications and Future Directions

The implications of leveraging machine learning for time series forecasting encompass both practical forecasters and theoretical researchers. Practically, these methods offer an improved toolkit for handling complex, high-dimensional dataset scenarios common in economics and finance. Theoretically, there is a call to further develop ML models tailored for dependent data structures, enhancing their robustness and interpretability.

The paper suggests several future research directions: developing robust inferential procedures for model selection in a high-dimensional landscape, leveraging unstructured data such as textual datasets for richer economic predictions, and improving model performance amidst structural breaks and regime changes.

In summary, the paper underscores the transformative potential of machine learning models in time series forecasting, particularly within economic and financial prognostications. By dissecting methodological innovations and providing substantive empirical evidence, it advances the understanding of how machine learning can effectively augment traditional econometric methodologies in data-rich environments.