Expert System for Bitcoin Forecasting: Integrating Global Liquidity via TimeXer Transformers
This addresses the problem of volatile long-horizon Bitcoin forecasting for financial analysts, but it is incremental as it builds on existing TimeXer architecture with added macroeconomic conditioning.
The paper tackled Bitcoin price forecasting by integrating Global M2 Liquidity as an exogenous variable, resulting in a model that reduced mean squared error by over 89% compared to a baseline at a 70-day horizon.
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.