Aug 2026· International Journal of Machine Learning and Cybernetics· Vol 17· 0 citations· 52 references
TL;DR
This work proposes FTSformer, a novel financial-initiated multi-scale exogenous-fusion framework for time series forecasting that substantially outperforms existing baselines in terms of forecasting accuracy, training stability, and robustness to exogenous perturbations.
Financial candlestick forecasting is fundamental to quantitative investment, yet it remains exceptionally challenging due to extremely low signal-to-noise ratios and vast heterogeneity across markets and instruments. Existing approaches have largely attempted to introduce deep learning to capture hidden temporal featur...
In order to make well-informed decisions, long-term time series forecasting is crucial for a number of applications in finance, energy, and environmental science. While traditional transformer models have demonstrated a strong ability to capture temporal dependencies, they frequently struggle to handle the lengthy sequ...
YongKyung Oh, Alex A. T. Bui· IISE Annual Conference &...· 0 citations
Time-series foundation models are increasingly adapted to new domains through fine-tuning on target data, under the implicit assumption that more target data yields better forecasts. We show that this assumption can fail in financial forecasting, where individual price changes are difficult to predict, but large moves...
Manh Nguyen, M. Nguyen, H. Nguyen et al.· 0 citations
A forecasting framework that explicitly models the coupling between trend and seasonality, and a heteroscedastic Laplace loss function that combines uncertainty weighting with heteroscedastic modeling, reducing the impact of error accumulation over long prediction horizons and enhancing robustness to outliers and heavy...
Zi-Qiong Li, He-Yu Chai, Xinru Liu et al.· IEEE Transactions on Neural...· 0 citations
PGA-Trans-HAR is developed, a neuro-econometric architecture that combines a rolling ridge-VAR/GFEVD predictive-connectedness network, masked spatio-temporal attention, and a frozen HAR anchor to improve multi-market volatility forecasts in asynchronous financial environments.
Commodity futures markets exhibit pronounced non-stationarity, nonlinearity, and multifractal characteristics that challenge traditional linear models. We employ a multiscale framework integrating four methodologies—MF-DCCA, PG irreversibility index, MSWPE, and JS-divergence segmentation—to analyze these features using...
Xia Zhao, Kai-Cheng Xie· Entropy· 0 citations
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