Partial multivariate transformer as a tool for cryptocurrencies time series prediction

ArXiv ID: 2512.04099 “View on arXiv”

Authors: Andrzej Tokajuk, Jarosław A. Chudziak

Abstract

Forecasting cryptocurrency prices is hindered by extreme volatility and a methodological dilemma between information-scarce univariate models and noise-prone full-multivariate models. This paper investigates a partial-multivariate approach to balance this trade-off, hypothesizing that a strategic subset of features offers superior predictive power. We apply the Partial-Multivariate Transformer (PMformer) to forecast daily returns for BTCUSDT and ETHUSDT, benchmarking it against eleven classical and deep learning models. Our empirical results yield two primary contributions. First, we demonstrate that the partial-multivariate strategy achieves significant statistical accuracy, effectively balancing informative signals with noise. Second, we experiment and discuss an observable disconnect between this statistical performance and practical trading utility; lower prediction error did not consistently translate to higher financial returns in simulations. This finding challenges the reliance on traditional error metrics and highlights the need to develop evaluation criteria more aligned with real-world financial objectives.

Keywords: Partial-Multivariate Transformer, Cryptocurrency forecasting, Deep learning, Feature selection, Trading simulation, Crypto Assets

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning mathematics (dual-attention transformers, tokenization, embeddings, MSE optimization) and demonstrates high empirical rigor through extensive benchmarking against 11 models, hyperparameter tuning, and explicit discussion of trading utility vs. statistical metrics.

  flowchart TD
    A["Research Goal:<br>Balance information-scarce vs.<br>noise-prone forecasting in<br>cryptocurrency time series"] --> B["Methodology: Partial-Multivariate Transformer<br>(PMformer) with<br>strategic feature subset selection"]
    B --> C["Data & Inputs:<br>BTCUSDT & ETHUSDT<br>Daily Returns"]
    C --> D["Computational Process:<br>Compare PMformer vs.<br>11 benchmarks (Classical &<br>Deep Learning models)"]
    D --> E{"Key Findings & Outcomes"}
    E --> F["1. Statistical Performance:<br>PMformer achieves significant<br>accuracy improvement"]
    E --> G["2. Practical Utility:<br>Disconnect observed:<br>Low error ≠ High trading returns"]
    E --> H["Implication:<br>Need for evaluation criteria<br>aligned with real-world<br>financial objectives"]