Comparative analysis of deep learning models for time series forecasting on Solana cryptocurrency data using Darts

This research presents a comparative analysis of several deep learning models for time series forecasting on Solana cryptocurrency data, using the Darts library. The study evaluates the performance of six models, Block RNN, N-BEATS, N-HiTS, RNN, TCN, and TFT, using both empirical and quantitative. The Block RNN model demonstrated the best overall performance, achieving the lowest error rates, while N-BEATS and TCN closely followed. N-HiTS and TFT models struggled with higher complexity and the relatively small dataset, leading to poor performance. However, further training of the N-BEATS model resulted in significant improvements, demonstrating its potential in capturing long-term trends in volatile cryptocurrency markets. This study provides valuable insights for selecting deep learning models suited to forecasting in such dynamic environments.