Paper: arXiv 2502.09079

Abstract

This paper offers a thorough examination of the univariate predictability in cryptocurrency time-series. By exploiting a combination of complexity measure and model predictions we explore the cryptocurrencies time-series forecasting task focusing on the exchange rate in USD of Litecoin, Binance Coin, Bitcoin, Ethereum, and XRP. On one hand, to assess the complexity and the randomness of these time-series, a comparative analysis has been performed using Brownian and colored noises as a benchmark. The results obtained from the Complexity-Entropy causality plane and power density spectrum analysis reveal that cryptocurrency time-series exhibit characteristics closely resembling those of Brownian noise when analyzed in a univariate context. On the other hand, the application of a wide range of statistical, machine and deep learning models for time-series forecasting demonstrates the low predictability of cryptocurrencies. Notably, our analysis reveals that simpler models such as Naive models consistently outperform the more complex machine and deep learning ones in terms of forecasting accuracy across different forecast horizons and time windows. The combined study of complexity and forecasting accuracies highlights the difficulty of predicting the cryptocurrency market. These findings provide valuable insights into the inherent characteristics of the cryptocurrency data and highlight the need to reassess the challenges associated with predicting cryptocurrency’s price movements.

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 8.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced mathematical methods like Permutation Entropy and the Complexity-Entropy causality plane (CH-plane) with Jensen-Shannon complexity, increasing math complexity. It demonstrates high empirical rigor through a multi-cryptocurrency study, use of multiple time windows, and rigorous comparison of statistical, ML, and DL models against naive baselines in a univariate forecasting task.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Quantify Crypto Predictability"] --> B["Data Input:<br>USD Exchange Rates of 5 Cryptos"]
  B --> C{"Complexity Analysis<br>vs. Noise Benchmarks"}
  C --> D["Complexity-Entropy<br>Causality Plane"]
  C --> E["Power Density<br>Spectrum"]
  D & E --> F{"Forecasting Analysis<br>Statistical, ML, and DL Models"}
  F --> G["Key Findings:<br>Cryptos resemble Brownian Noise"]
  F --> H["Key Findings:<br>Simpler models (Naive) outperform<br>complex ML/DL models"]

Cited References (33)

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  8. Complexity analysis and forecasting of variations in cryptocurrency trading volume with support vector regression tuned by Bayesian optimization under different kernels: An empirical comparison from a large dataset (2022) — link
and 25 more
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0 of 33 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.