Explainable Prediction of Economic Time Series Using IMFs and Neural Networks

Paper: arXiv 2512.12499

Authors: Pablo Hidalgo, Julio E. Sandubete, Agustín García-García

Abstract

This study investigates the contribution of Intrinsic Mode Functions (IMFs) derived from economic time series to the predictive performance of neural network models, specifically Multilayer Perceptrons (MLP) and Long Short-Term Memory (LSTM) networks. To enhance interpretability, DeepSHAP is applied, which estimates the marginal contribution of each IMF while keeping the rest of the series intact. Results show that the last IMFs, representing long-term trends, are generally the most influential according to DeepSHAP, whereas high-frequency IMFs contribute less and may even introduce noise, as evidenced by improved metrics upon their removal. Differences between MLP and LSTM highlight the effect of model architecture on feature relevance distribution, with LSTM allocating importance more evenly across IMFs.

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 4.0/10
• Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper employs advanced mathematical concepts including Empirical Mode Decomposition, neural network architectures (MLP/LSTM), and formal SHAP value derivations with iterative formulas. While it uses real economic time series data and standard libraries for implementation, the description focuses more on methodological framework and interpretability results rather than providing extensive backtesting details, performance metrics, or direct trading implications.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Explain IMF contributions<br>to NN forecasting"] --> B["Preprocessing<br>Decompose economic series into IMFs"]
  B --> C["Modeling<br>Train MLP and LSTM models"]
  C --> D["Explainability<br>Apply DeepSHAP to quantify IMF importance"]
  D --> E["Analysis & Outcomes<br>Long-term trend IMFs dominate importance"]
  D --> F["Analysis & Outcomes<br>High-frequency IMFs add noise & may be removed"]
  D --> G["Analysis & Outcomes<br>LSTM shows more even importance distribution"]
  E & F & G --> H["Conclusion<br>IMFs provide interpretable feature basis<br>for NN time series forecasting"]

Cited References (18)

1. COVID-19 Media Chatter and Macroeconomic Reflectors on Black Swan: A Spanish and Indian Stock Markets Comparison (2023) — link
2. Analysis of financial pressure impacts on the health care industry with an explainable machine learning method: China versus the USA (2022) — link
3. TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models (2021) — link
4. Understanding Global Feature Contributions With Additive Importance Measures (2020)
5. Deep Learning for Time Series Forecasting: Tutorial and Literature Survey (2020) — link
6. From Local Explanations to Global Understanding with Explainable AI for Trees (2020) — link
7. AR-Net: A simple Auto-Regressive Neural Network for time-series (2019) — link
8. Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions (2019) — link

0 of 18 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.