A time series-based financial trend intelligent analysis and prediction system
By constructing a fragment library and utilizing multi-scale decomposition and semantic encoding techniques, the shortcomings of existing financial forecasting methods in capturing and adapting to complex trends are addressed, achieving accurate prediction and interpretability, and enabling an intelligent financial trend analysis and forecasting system that adapts to new trend changes.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Filing Date
- 2026-04-15
- Publication Date
- 2026-07-17
AI Technical Summary
Existing financial forecasting methods struggle to accurately capture complex and ever-changing financial trend patterns, lack interpretability, and are poorly adaptable to new trends.
By constructing a fragment library and utilizing multi-scale decomposition and semantic encoding techniques, financial time series data is decomposed into high-frequency, mid-frequency, and low-frequency components. A pre-trained semantic encoder is used to map the data into a comprehensive semantic vector, and an online matching prediction is performed through a similarity calculation model. An adaptive update mechanism is combined to dynamically adjust the model to adapt to new trends.
It achieves accurate capture and prediction of complex financial trends, provides interpretable forecast results, maintains high efficiency and adaptability in the face of new trend changes, and reduces model update costs.
Smart Images
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