TRiP-Net: Risk-driven adaptive patching for financial time series forecasting

Financial time series forecasting remains a challenging problem due to the clear non-stationarity of financial data, complex temporal dependencies, and frequent local fluctuations. Although Transformer-based models have achieved good performance in recent years, most methods still use fixed-length patch partitioning, which cannot adapt well to changing volatility patterns in financial sequences. To address this issue, this paper proposes a new forecasting framework named TRiP-Net. The model introduces TRiP, a risk-aware adaptive patching strategy that adjusts patch granularity based on local risk characteristics within the sequence. This allows the model to better capture local dynamic changes in financial time series. In addition, a DCN module is used to improve state-aware feature normalisation under different market regimes. Experimental results show that, under multiple forecasting horizons, TRiP-Net outperforms several mainstream baselines, including PatchTST, Pathformer, and iTransformer, in terms of MAE, RMSE, and MAPE. Ablation studies further confirm the effectiveness of the proposed modules and the adaptive patching strategy. Overall, the results show that TRiP-Net improves the accuracy of multi-step financial time series forecasting.