AI Model Predicts Volatile Month Ahead for Bitcoin

The prediction was generated using a WaveNet deep learning model built on GluonTS/MXNet. Trained on data spanning from 2012 to 2025, the model integrated 399 distinct on-chain indicators, including price trends, exchange flows, miner activity, fee metrics, and network volume.

Forecast Suggests Market Uncertainty Remains Elevated

The WaveNet model does not provide a single-price projection. Instead, it offers a range-based forecast, showing a median expected price path alongside 50% and 90% confidence intervals. These bands reveal just how unpredictable short-term BTC movements could be.

While the overall historical trend used in this period leans slightly downward, the model’s wide projection range captures the influence of macro uncertainty and volatile on-chain signals.

Data-Driven, Not Manually Tuned for Timing

CryptoOnchain emphasized that the forecast is fully data-driven, not tweaked for short-term trading calls. All model inputs came from CryptoQuant’s on-chain database, ensuring the prediction reflects actual network behavior rather than speculative bias.

This method adds robustness, accounting for hidden shifts in miner dynamics, exchange behavior, and investor flows.

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Implication: Traders Should Prepare for Broad Price Swings

The model’s wide confidence intervals may signal unstable conditions through June. For market participants, this means higher potential rewards—but also greater risk.

Traders and analysts may want to approach the next few weeks with caution, as both fundamental and technical signals suggest that volatility could define the short-term outlook for Bitcoin.

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