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TRiP-Net: Risk-driven adaptive patching for financial time series forecasting
Wuzhida Bao1, Guangyang Tian1, Yuting Cao2
1Australian AI Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, 2007, Australia.
Summary
TRiP-Net introduces a novel risk-aware adaptive patching strategy for financial time series forecasting. This method significantly improves multi-step forecasting accuracy by adapting to changing market volatility.
Area of Science:
- Quantitative Finance
- Machine Learning
- Time Series Analysis
Background:
- Financial time series forecasting is complex due to non-stationarity and volatility.
- Existing Transformer models struggle with fixed-length data partitioning, failing to adapt to dynamic financial patterns.
Purpose of the Study:
- To propose TRiP-Net, a novel framework for accurate multi-step financial time series forecasting.
- To introduce a risk-aware adaptive patching strategy (TRiP) that adjusts to local risk characteristics.
Main Methods:
- TRiP-Net employs a risk-aware adaptive patching strategy (TRiP) to dynamically adjust patch granularity.
- A Depthwise Convolutional Network (DCN) module enhances state-aware feature normalization across market regimes.
Main Results:
- TRiP-Net demonstrated superior performance over baselines like PatchTST, Pathformer, and iTransformer.
- Significant improvements were observed in Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE) across multiple forecasting horizons.
- Ablation studies validated the effectiveness of the TRiP strategy and DCN module.
Conclusions:
- TRiP-Net effectively captures local dynamic changes in financial time series.
- The proposed adaptive patching and normalization methods enhance the accuracy of multi-step financial forecasting.
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