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

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.…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStock Market Forecasting Methods · Blockchain Technology Applications and Security · Financial Distress and Bankruptcy Prediction
