# Partial multivariate transformer as a tool for cryptocurrencies time series prediction

arXiv: https://arxiv.org/abs/2512.04099 · HTML: https://mlsanity.com/paper/2512.04099/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 32 references that are in the map.
- First posted on arXiv 22 Nov 2025.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [iTransformer: Inverted Transformers Are Effective for Time Series Forecasting](https://mlsanity.com/paper/2310.06625/) (2023)
- [ElliottAgents: A Natural Language-Driven Multi-Agent System for Stock Market Analysis and Prediction](https://mlsanity.com/paper/2507.03435/) (2025)
- [Applying Informer for Option Pricing: A Transformer-Based Approach](https://mlsanity.com/paper/2506.05565/) (2025)
- [Partial-Multivariate Model for Forecasting](https://mlsanity.com/paper/2408.09703/) (2024)
- [Quantifying Cryptocurrency Unpredictability: A Comprehensive Study of Complexity and Forecasting](https://mlsanity.com/paper/2502.09079/) (2025)

## Built on by (most cited first, 3 of 3)

- [Modeling Decisions in Blockchain Analytics: A Leakage-Aware Evaluation of Tree-Based vs. Sequential Models](https://mlsanity.com/paper/2607.27350/) (2026)
- [Compression-Based Behavioral Similarity for Open-World Sybil Discovery on Ethereum](https://mlsanity.com/paper/2607.27370/) (2026)
- [Loss Choice or Model Choice? The Role of Forecast Level in Cryptocurrency Volatility Forecasting](https://mlsanity.com/paper/2609.27024/) (2026)

## Abstract (arXiv)

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.