Decoding Bitcoin: A New Neural Network for Price Forecasting

Author: Denis Avetisyan

Researchers have developed a parallel gated recurrent unit (GRU) architecture that offers improved accuracy and efficiency in predicting Bitcoin prices.

The proposed PGRU architecture leverages two parallel GRU networks-one for price features and another for structural features-whose fused outputs, processed by a feedforward network, ultimately generate price predictions.

This study introduces a novel deep learning approach combining price data with on-chain blockchain features to enhance time-series forecasting of cryptocurrency values.

Predicting volatile cryptocurrency prices remains a significant challenge in financial forecasting despite numerous proposed methods. This paper introduces a novel deep learning approach, ‘Cryptocurrency Price Prediction Using Parallel Gated Recurrent Units’, designed to improve the accuracy and efficiency of Bitcoin price forecasting. By leveraging parallel gated recurrent units and integrating both price data and blockchain characteristics, the proposed model achieves lower mean absolute percentage errors and reduced computational costs compared to existing techniques. Could this parallel architecture represent a scalable solution for time-series forecasting in other dynamic financial markets?

Decoding Bitcoin’s Price Volatility

The inherent volatility of Bitcoin presents a formidable obstacle to reliable price prediction. Unlike established financial instruments with decades of historical data and relatively stable influencing factors, Bitcoin’s value is subject to rapid and often unpredictable swings driven by factors ranging from regulatory news and technological advancements to social media sentiment and macroeconomic events. Consequently, simplistic forecasting methods frequently falter, necessitating the development of sophisticated predictive models capable of accommodating nonlinear dynamics and incorporating a broader spectrum of potentially relevant variables. These models must move beyond traditional time-series analysis, embracing techniques like machine learning and artificial neural networks to discern patterns and anticipate future price movements within this uniquely turbulent asset class. Accurate forecasting isn’t merely an academic pursuit; it’s crucial for investors seeking to manage risk and capitalize on opportunities within the evolving cryptocurrency landscape.

Conventional time-series analyses, designed for relatively stable systems, frequently falter when applied to Bitcoin’s price fluctuations. These methods often assume linear relationships and consistent statistical properties, but Bitcoin’s value is shaped by a confluence of factors – market sentiment, regulatory news, technological advancements, and macroeconomic conditions – that introduce significant nonlinearity. This means that past price movements are a poor predictor of future ones, as the system’s behavior changes unpredictably. The inherent complexity and sensitivity to external shocks render traditional forecasting techniques, like moving averages or autoregressive models, inadequate for capturing the true dynamics at play, necessitating the exploration of more sophisticated approaches such as machine learning and agent-based modeling to navigate the intricate patterns influencing Bitcoin’s valuation.

An LSTM-based model accurately predicts Bitcoin prices over a 10-day horizon, as demonstrated by the close correspondence between predicted and actual values.

The Power of Machine Learning in Financial Forecasting

Traditional statistical methods for financial forecasting, such as linear regression and ARIMA models, often struggle to capture the non-linear and complex relationships inherent in financial time series data. Machine learning algorithms, conversely, offer a wider range of modeling capabilities, including the ability to identify and exploit these non-linear patterns without requiring explicit pre-specification. This is achieved through algorithms that learn directly from data, adjusting internal parameters to minimize prediction errors. Techniques like support vector machines, decision trees, and, increasingly, neural networks, can model intricate dependencies and interactions between various financial indicators, potentially leading to improved forecast accuracy compared to traditional linear approaches. Furthermore, machine learning models can readily incorporate a larger number of predictor variables and adapt to changing market conditions through continuous retraining.

Deep Neural Networks (DNNs) have gained prominence in financial time-series analysis due to their capacity to model non-linear relationships and complex interactions within data. Unlike traditional statistical methods which often rely on predefined features and assumptions about data distribution, DNNs automatically learn hierarchical representations directly from raw input, such as historical price data, volume, and technical indicators. This capability allows them to identify subtle patterns and dependencies that might be missed by simpler models. The increased availability of large financial datasets, coupled with advancements in computational power and optimization algorithms, has facilitated the training of DNNs with millions of parameters, enabling them to capture intricate market dynamics. Common DNN architectures employed include Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs) – often used for pattern recognition in time-series images – and, increasingly, Recurrent Neural Networks (RNNs) designed specifically for sequential data processing.

Recurrent Neural Networks (RNNs) address the limitations of traditional neural networks when applied to time-series data by incorporating a feedback loop. This internal memory allows the network to consider previous inputs in the sequence when processing current data, effectively modeling temporal dependencies. Unlike feedforward networks that treat each data point independently, RNNs maintain a hidden state that is updated at each time step, representing information about the past. Variants like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) further refine this process by mitigating the vanishing gradient problem, enabling the network to learn long-range dependencies within financial price histories and improve forecasting accuracy. The sequential nature of RNNs directly aligns with the chronological order of financial data, making them a preferred architecture for tasks like stock price prediction, algorithmic trading, and risk management.

An LSTM-based model accurately predicts Bitcoin prices with a window of <span class="katex-eq" data-katex-display="false">w=20</span>. — An LSTM-based model accurately predicts Bitcoin prices with a window of $w=20$ .

A Parallel GRU Network for Enhanced Predictive Capability

The proposed network architecture employs two Gated Recurrent Unit (GRU) networks operating in parallel. Each GRU network processes a separate feature set: one dedicated to Price Features and the other to Structural Features derived from the Bitcoin blockchain. This parallel configuration allows for simultaneous processing of distinct data streams, potentially reducing computational latency and improving model responsiveness. The outputs of both GRU networks are then combined to generate a final prediction, leveraging the complementary information contained within each feature set. This differs from traditional sequential GRU models which process all features through a single recurrent layer.

Gated Recurrent Units (GRUs) are recognized for their ability to effectively process sequential data, mitigating the vanishing gradient problem common in traditional Recurrent Neural Networks. By employing a parallel architecture with two GRU networks, the proposed model leverages this sequential processing capability while simultaneously increasing computational throughput. This parallelization allows for the independent processing of distinct feature sets, reducing the overall processing time and enabling the model to handle larger datasets more efficiently. The combined effect is an improvement in both the speed and scalability of prediction tasks compared to single-GRU or conventional RNN architectures.

The prediction model utilizes a dual-input feature set comprised of Price Features and Structural Features extracted from the Bitcoin blockchain. Price Features encompass historical price data, trading volume, and related market indicators. Structural Features, conversely, are derived directly from the blockchain itself, including block size, transaction count, average block time, and network hash rate. This combined approach aims to move beyond solely relying on market data by incorporating on-chain metrics that reflect the underlying network activity and potentially influence price movements, thereby providing a more comprehensive assessment of influencing factors than models using either feature set in isolation.

A parallel LSTM network architecture with a fusion layer serves as a baseline for evaluating the performance of the PGRU model.

Rigorous Data Preparation and Model Validation

Z-Score Normalization, also known as standardization, was applied to both Price Features and Structural Features as a preprocessing step prior to model training. This transformation rescales each feature to have a mean of 0 and a standard deviation of 1. The calculation involves subtracting the mean of the feature from each data point and then dividing by the standard deviation: $(x - \mu) / \sigma$ , where x is the data point, μ is the mean, and σ is the standard deviation. This standardization addresses potential issues arising from features having different scales, preventing features with larger values from disproportionately influencing the model during training and thereby improving the speed and stability of model convergence.

A sliding window technique was implemented to convert the time-series data into a supervised learning format. This involved creating pairs of data points where a window of historical data served as the input feature set, and the subsequent data point represented the target variable. The window was moved forward one step at a time across the entire time series, generating multiple input-target pairs. This process effectively creates a dataset of independent samples suitable for training a predictive model, addressing the inherent sequential nature of the time-series data and allowing the model to learn relationships between past observations and future values.

Ten-Fold Cross-Validation was implemented to assess the model’s predictive performance and its ability to generalize to unseen data. The dataset was partitioned into ten equally sized subsets, or ‘folds’. The model was then trained on nine of these folds, and evaluated on the remaining fold. This process was repeated ten times, with a different fold used for evaluation each time. The performance metrics – including mean squared error and R-squared – were averaged across all ten iterations to provide a robust and statistically reliable estimate of the model’s overall generalization capability, minimizing the risk of overfitting and providing a more accurate representation of real-world performance.

The LSTM-based model demonstrates prediction errors of up to <span class="katex-eq" data-katex-display="false">w=20</span> when estimating true prices. — The LSTM-based model demonstrates prediction errors of up to $w=20$ when estimating true prices.

Demonstrated Accuracy and Broader Implications for Financial Modeling

The predictive capability of the forecasting model underwent a stringent evaluation process, employing established statistical measures to quantify its accuracy. Specifically, researchers utilized Mean Squared Error $\text{MSE} = \frac{1}{n}\sum_{i=1}^{n}(y_i - \hat{y}_i)^2$ , Mean Absolute Error $\text{MAE} = \frac{1}{n}\sum_{i=1}^{n}|y_i - \hat{y}_i|$ , and Root Mean Squared Error $\text{RMSE} = \sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_i - \hat{y}_i)^2}$ to assess the difference between predicted and actual Bitcoin prices. These metrics provided a comprehensive understanding of the model’s performance, highlighting its ability to minimize errors and offer reliable forecasts, and enabling a robust comparison against alternative forecasting techniques. The consistent and favorable results across these measures validated the model’s efficacy and underscored the quality of its predictions.

Rigorous testing reveals the Parallel GRU Network architecture successfully forecasts Bitcoin prices with a high degree of accuracy. The model achieved a Mean Absolute Percentage Error (MAPE) of 3.24% when predicting values using a 20-period window, and further improved to 2.64% with a 15-period window. These metrics indicate a substantial reduction in forecasting error compared to traditional methods, suggesting the network effectively captures the complex, non-linear dynamics inherent in Bitcoin’s price fluctuations. This precision offers a valuable tool for financial analysis and potentially allows for more informed, data-driven strategies in the volatile cryptocurrency market.

The demonstrated forecasting capabilities extend beyond Bitcoin, offering a robust framework applicable to diverse financial instruments and markets. This methodology allows for the potential development of more sophisticated risk management strategies by providing earlier and more accurate predictions of price fluctuations. Consequently, investors and financial institutions can leverage these insights to optimize portfolio allocation, refine trading strategies, and ultimately make more informed investment decisions, potentially leading to improved returns and reduced exposure to market volatility. The adaptability of the Parallel GRU Network architecture suggests a future where predictive modeling plays an increasingly central role in navigating the complexities of global finance.

The PGRU model accurately predicts Bitcoin prices over a 10-day horizon with a window size of <span class="katex-eq" data-katex-display="false">w=20</span>. — The PGRU model accurately predicts Bitcoin prices over a 10-day horizon with a window size of $w=20$ .

The pursuit of accurate time-series forecasting, as demonstrated in this work with parallel GRU networks, demands a holistic understanding of the system being modeled. The architecture’s success isn’t merely a result of sophisticated algorithms, but a deliberate integration of both price data and structural blockchain features. This echoes Grace Hopper’s sentiment: “It’s easier to ask forgiveness than it is to get permission.” The researchers, in a sense, ‘forged ahead’ combining unconventional data points – blockchain structure – with traditional price analysis. This willingness to experiment, to move beyond established norms, reveals that simplification isn’t about reducing complexity, but rather about discerning the essential components that drive meaningful results, mirroring the study’s focus on optimized computational cost and error rates.

What’s Next?

The pursuit of accurate time-series forecasting, even with architectures as nuanced as parallel GRUs, often feels like polishing brass on a sinking ship. This work demonstrates a measurable improvement in Bitcoin price prediction, yet the underlying instability of the asset itself remains. If the system survives on duct tape – combining price history with on-chain metrics – it’s probably overengineered. The true limitation isn’t algorithmic; it’s the assumption that past performance reliably indicates future outcomes in a fundamentally speculative market.

Future research should shift focus from merely predicting price to modeling the conditions that drive volatility. Modularity without context is an illusion of control; simply adding more features to the GRU will yield diminishing returns. A more fruitful approach lies in understanding the interplay between blockchain structure, network effects, and macroeconomic forces. The network isn’t a black box to be optimized; it’s a complex adaptive system demanding holistic analysis.

Ultimately, the value of this work may not be in its predictive power, but in its highlighting the inherent limits of prediction itself. The elegance of a well-designed algorithm reveals the chaos it attempts to tame. The goal shouldn’t be to eliminate uncertainty, but to build systems resilient enough to navigate it – systems that gracefully degrade rather than catastrophically fail when faced with the inevitable unpredictable event.

Original article: https://arxiv.org/pdf/2512.22599.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/