Expert System for Bitcoin Forecasting: Integrating Global Liquidity via TimeXer Transformers

TL;DR

This paper introduces a deep learning model that incorporates global liquidity data to improve long-term Bitcoin price forecasts, significantly outperforming existing models in accuracy.

Contribution

It presents a novel integration of macroeconomic liquidity data with the TimeXer architecture for enhanced Bitcoin forecasting accuracy.

Findings

TimeXer-Exog outperforms benchmarks by over 89% in MSE at 70-day horizon.

Explicit macroeconomic conditioning stabilizes long-horizon forecasts.

Global liquidity integration significantly improves Bitcoin price prediction.

Abstract

Bitcoin price forecasting is characterized by extreme volatility and non-stationarity, often defying traditional univariate time-series models over long horizons. This paper addresses a critical gap by integrating Global M2 Liquidity, aggregated from 18 major economies, as a leading exogenous variable with a 12-week lag structure. Using the TimeXer architecture, we compare a liquidity-conditioned forecasting model (TimeXer-Exog) against state-of-the-art benchmarks including LSTM, N-BEATS, PatchTST, and a standard univariate TimeXer. Experiments conducted on daily Bitcoin price data from January 2020 to August 2025 demonstrate that explicit macroeconomic conditioning significantly stabilizes long-horizon forecasts. At a 70-day forecast horizon, the proposed TimeXer-Exog model achieves a mean squared error (MSE) 1.08e8, outperforming the univariate TimeXer baseline by over 89 percent. These results highlight that conditioning deep learning models on global liquidity provides substantial improvements in long-horizon Bitcoin price forecasting.