ChanFormer: Encoder-Only Channel-Wise Transformer Advances Multi-Asset Financial Time Series Forecasting

Understanding the Challenges in Financial Time Series Forecasting

Financial markets, particularly cryptocurrencies, present unique difficulties for predictive models. Prices exhibit high volatility, frequent distributional shifts known as non-stationarity, and evolving correlations between different assets. Traditional statistical methods and even many modern machine learning approaches often fall short because they struggle to capture these dynamic inter-asset relationships while maintaining stability across changing market regimes. Open-high-low-close-volume, or OHLCV, data for multiple assets adds further dimensionality, with each asset contributing five channels of information.

Researchers have long explored neural network architectures to address these issues. Recurrent models like long short-term memory networks capture sequential patterns but can suffer from vanishing gradients over long horizons. More recently, Transformer models, originally developed for natural language processing, have shown promise in time series tasks through their self-attention mechanisms that weigh the importance of different positions in a sequence.

The ChanFormer Architecture Explained

ChanFormer takes an encoder-only approach centered on channels rather than time patches. Instead of dividing historical sequences into patches along the time dimension, the model projects the entire lookback window for each individual channel into a single high-dimensional token. This design isolates the modeling of long-range temporal dependencies within the projection step and shifts focus to interactions across assets.

A standard Transformer encoder then processes these channel tokens using cross-channel self-attention. This allows the model to learn dependencies between different assets dynamically. Following the encoder, a channel-mixing multi-layer perceptron fuses information across channels in a non-linear fashion to produce the final forecasts.

To combat non-stationarity, the framework incorporates reversible instance normalization, or RevIN, applied at both input and output stages. RevIN normalizes each channel independently and reverses the process during prediction, helping the model adapt to shifting data distributions without losing information.

The architecture simultaneously generates one-step-ahead predictions for 50 OHLCV channels across groups of 10 cryptocurrencies or 10 stocks. With a typical 90-day historical window, embedding dimension of 384, six attention heads, and four encoder layers, the model balances expressiveness with efficiency.

Empirical Performance on Cryptocurrency and Stock Datasets

Evaluations across cryptocurrency and equity datasets demonstrate strong results. In cryptocurrency tests, ChanFormer achieved the lowest mean absolute error and root mean square error for all 10 assets examined. It outperformed leading baselines such as iTransformer and PatchTST by substantial margins, delivering an average error reduction of 29 percent relative to the strongest competing models. Compared with classical statistical approaches, improvements reached 83 to 94 percent.

Stock market experiments showed competitive performance, though gains were most pronounced in the more volatile cryptocurrency setting. The model handled regime shifts effectively, maintaining accuracy where other methods degraded.

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Backtesting Results and Trading Implications

Beyond accuracy metrics, the researchers conducted backtesting with a long-short trading strategy. Over a 364-day period, the approach produced positive total returns and a Sharpe ratio of 1.27. The Sharpe ratio measures risk-adjusted return, indicating that the strategy delivered meaningful profits relative to the volatility experienced. These outcomes suggest practical utility for portfolio managers seeking data-driven signals in fast-moving markets.

Computational Efficiency and Environmental Considerations

Practical deployment requires attention to resource demands. ChanFormer completed training in approximately 23 minutes and generated inferences in about 1.6 seconds per batch. Total energy consumption stood at 0.107 kilowatt-hours, corresponding to estimated carbon emissions of 0.051 kilograms of CO2 equivalent. Such figures support sustainable use in research and production environments where repeated model updates occur.

Code Availability and Reproducibility

The authors have made the source code publicly available through a GitHub repository maintained under TheQuantScientist. The repository includes implementations for the three experimental scenarios—cryptocurrency only, stocks only, and blended—along with data processing pipelines, ablation studies, and backtesting scripts. Datasets cover specific assets such as ATOMUSDT, SUIUSDT for crypto and AAPL, NVDA for equities, with daily OHLCV records aligned across timestamps.

Researchers and practitioners can access the full paper at https://www.sciencedirect.com/science/article/abs/pii/S0045790626003988. An earlier version also appears on SSRN under abstract ID 6247718.

Broader Implications for AI in Finance and Academic Research

ChanFormer illustrates how targeted architectural choices—channel-centric tokenization combined with reversible normalization—can yield gains in domains characterized by strong cross-entity dependencies and distributional drift. The approach provides a controlled baseline for comparing against patch-based Transformers, highlighting the value of explicit cross-asset modeling.

For academics in computer science, finance, and quantitative methods, the work opens avenues for extending channel-wise designs to other multivariate forecasting tasks, such as macroeconomic indicators or supply chain data. University laboratories focused on machine learning applications may find the low computational footprint attractive for student projects and thesis work.

Industry stakeholders, including hedge funds and fintech firms, could explore integration into existing risk and alpha generation pipelines, particularly where multi-asset portfolios require joint forecasting rather than isolated univariate predictions.

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Future Research Directions

The paper outlines several promising extensions. Incorporating multimodal inputs such as news sentiment or on-chain metrics could further enhance robustness. Exploring continual learning strategies might help models adapt to entirely new market regimes without full retraining. Interpretability techniques applied to the cross-channel attention maps could yield insights into which asset relationships drive predictions at different times.

Scaling the framework to larger asset universes or higher-frequency data represents another logical step. Hybrid models that blend ChanFormer components with linear or econometric modules may improve transparency while retaining predictive power.

Conclusion

The release of ChanFormer contributes a refined tool to the growing toolkit of Transformer variants for financial applications. By emphasizing channel-wise processing and stabilization techniques, the authors deliver measurable improvements in accuracy, profitability in simulation, and operational efficiency. Academics, students, and professionals interested in the intersection of deep learning and quantitative finance will find the open resources and detailed methodology valuable for further exploration and adaptation.