Temporal Pattern Analysis and Transaction Volume Trends in the Ripple (XRP) Network Using Time Series Analysis - Aljohani, Riyadh Abdulhadi M; Alnahdi, Abdulaziz Amir (Journal of Current Research in Blockchain, 2025)
This study analyzes the temporal patterns and transaction volume trends in the Ripple (XRP) network using time series analysis. The dataset comprises over 1.2 million transactions spanning three years, allowing for a comprehensive examination of long-term trends and seasonal fluctuations. Summary statistics reveal a right-skewed distribution of transaction volume, where a majority of transactions involve relatively small amounts, while a few high-value transactions contribute disproportionately to overall network activity. Time series decomposition identifies a clear upward trend in transaction volume, with notable seasonal patterns corresponding to weekly and monthly cycles. These periodic trends suggest institutional trading behaviors, liquidity management strategies, and external market influences. Comparative forecasting analysis between ARIMA and LSTM models demonstrates that LSTM achieves superior predictive accuracy, with a 30% lower Mean Absolute Error (MAE) and a 25% reduction in Root Mean Squared Error (RMSE) compared to ARIMA. These results highlight the effectiveness of deep learning in capturing non-linear transaction dynamics within the blockchain ecosystem. Furthermore, anomaly detection using Isolation Forest successfully identifies transactional irregularities, particularly during periods of high market volatility and regulatory shifts. Several anomalous transaction spikes coincide with major market events, such as sudden exchange inflows and network congestion, reinforcing the role of external factors in influencing transaction activity. These findings emphasize the need for advanced forecasting techniques and real-time anomaly detection systems to improve transaction monitoring and enhance security within blockchain networks. Future research could integrate additional on-chain metrics, off-chain factors, and alternative deep learning models to refine predictive capabilities and support more resilient blockchain analytics frameworks.