Sample Reconstruction and Prediction of Stock Time Series Based on Trend Structure Extraction

Financial time series are characterized by strong noise, frequent fluctuations, and dense local trend switching, making raw-sequence prediction difficult. This paper investigates stock time-series prediction from the perspective of pre-training sample construction and proposes Multi-step Trend Structure Extraction (MTSE). MTSE identifies local highs and lows in price and related indicator series under different step sizes and combinations, transforming continuous observations into trend-event representations. The occurrence and confirmation positions of events are distinguished, confirmed price-turning events are used as sample anchors, and the latest confirmed events in volume, RSI, and BIAS available before the decision point are aligned within a unified decision table. Using ChiNext Index data at one-minute, 15-minute, 60-minute, daily, and weekly frequencies, we compare a Raw sliding-window pipeline with an MTSE reconstruction pipeline under CatBoost, Transformer, and CNN models. Paired statistical tests, layered ablations, and net-of-cost backtesting are used for evaluation. The results show that MTSE changes sample size, label-switching frequency, and trend-segment organization. Relative to Raw inputs, MTSE improves F1, AUC, and MCC in most models and time granularities. Ablation results indicate that the gains are associated with the joint organization of event filtering, structural information, and multi-indicator alignment. In net-of-cost backtesting, the MTSE pipeline generally provides better risk–return profiles. These findings suggest that pre-training trend-structure reconstruction can improve model learning and financial usability in the ChiNext Index setting.