基于Solana数字资产数据集的时间序列预测
Time Series Forecasting based on Solana Digital Asset Dataset
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中文总结 AI 辅助
本文提出首个Solana数字资产时间序列预测数据集，通过分析发现市场活动峰值与代币事件相关，经实验验证数据集信号有效，PatchTST在市值预测中表现最佳。
中文摘要 AI 辅助
对Solana数字资产进行准确分析和预测，需要同时捕捉代币层面行为和生态系统层面去中心化交易所（DEX）活动的数据。据我们所知，本文介绍了首个专为预测和市场结构分析设计的Solana数字资产时间序列数据集。该数据集包含1584个代币，时间分辨率为每日，观测时段为2024年3月24日至2025年3月16日，涵盖27个变量，包括代币交易、价格、流动性池余额、交易者活动、Solana DEX交易量、DEX交易者数量、新创建交易对以及SOL价格指标。我们未将该数据集仅用作模型比较的输入，而是用其表征生态系统快速增长时期由DEX驱动的代币市场。分析发现，2024年11月中旬和2025年1月中旬，DEX交易量、代币交易量、活跃钱包、买家、卖家、新交易者、流动性池余额及SOL价格均出现同步的全市场活动峰值。2025年1月的峰值与“Trump”代币事件同时发生，伴随从流动性积累到撤出的明显转变，表明单个代币动态与更广泛的Solana市场情绪及DEX活动密切相关。随后的预测实验用于实证验证数据集的信号内容：在三天期市值预测中，PatchTST取得最佳总体排名，微调后的Chronos紧随其后，统计基线对趋势主导型代币仍具竞争力。特征重要性分析进一步显示，SOL价格、SOL移动平均线、总DEX交易量、DEX交易者数量及新创建交易对是最具信息性的协变量。因此，主要贡献是整理出Solana预测数据集，并对塑造代币波动性的生态系统层面因素进行了数据驱动分析。
英文摘要
Accurate analysis and forecasting of Solana digital assets require data that captures both token-level behavior and ecosystem-level DEX activity. This paper introduces, to the best of our knowledge, the first Solana digital asset time series dataset designed for forecasting and market-structure analysis. The dataset contains 1,584 tokens observed at daily resolution from March 24, 2024 to March 16, 2025, with 27 variables combining token transactions, prices, liquidity-pool balances, trader activity, Solana DEX volume, DEX trader counts, newly created pairs, and SOL price indicators. Rather than treating the dataset only as input for model comparison, we use it to characterize the DEX-driven token market during a period of rapid ecosystem growth. The analysis identifies synchronized market-wide activity peaks in mid-November 2024 and mid-January 2025 across DEX volume, token trading volume, active wallets, buyers, sellers, new traders, liquidity-pool balances, and SOL price. The January 2025 peak coincides with the 'Trump' token event and is accompanied by a visible transition from liquidity accumulation to withdrawals, suggesting that individual token dynamics are strongly coupled to broader Solana market sentiment and DEX activity. Forecasting experiments are then used as an empirical validation of the dataset's signal content. In three-day-ahead market-capitalization prediction, PatchTST achieves the best overall rank, fine-tuned Chronos follows closely, and statistical baselines remain competitive for trend-dominated tokens. Feature-importance analysis further shows that SOL price, SOL moving averages, total DEX volume, DEX trader counts, and newly created pairs are among the most informative covariates. The main contribution is therefore a curated Solana forecasting dataset and a data-driven analysis of the ecosystem-level factors that shape token volatility.
