Expert System for Bitcoin Forecasting: Integrating Global Liquidity via TimeXer Transformers
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866911367946043392 |
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| author | T, Sravan Karthick |
| author_facet | T, Sravan Karthick |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22326 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Expert System for Bitcoin Forecasting: Integrating Global Liquidity via TimeXer Transformers T, Sravan Karthick Machine Learning Artificial Intelligence 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. |
| title | Expert System for Bitcoin Forecasting: Integrating Global Liquidity via TimeXer Transformers |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2512.22326 |