通过智能选择时间序列起点，提升预测准确率。
Optimal starting point for time series forecasting
- 用XGBoost和LightGBM动态调整序列长度，找最佳起点。
- 在M4等数据集上，新方法预测误差显著低于全量数据。
- 可与现有模型无缝结合，适合有数据漂移的场景。
近期时间序列预测研究多聚焦于模型本身改进。然而，当时间序列存在结构性断裂或概念漂移时，预测性能可能大幅下降。本文提出一种名为最优起点时间序列预测（OSP-TSP）的新方法，可与现有预测模型结合使用。该方法通过XGBoost和LightGBM模型调整序列长度，自动确定时间序列的最佳起始点（OSP），从而提升基础模型的预测性能。在M4数据集及其他真实世界数据集上的综合实证分析表明，基于OSP-TSP方法的预测结果始终优于使用完整时间序列数据的预测。此外，与现有模型结合后，预测精度进一步提升，验证了该方法的有效性与优势。
原文摘要 · Abstract (English)
Recent advances on time series forecasting mainly focus on improving the forecasting models themselves. However, when the time series data suffer from potential structural breaks or concept drifts, the forecasting performance might be significantly reduced. In this paper, we introduce a novel approach called Optimal Starting Point Time Series Forecast (OSP-TSP) for optimal forecasting, which can be combined with existing time series forecasting models. By adjusting the sequence length via leveraging the XGBoost and LightGBM models, the proposed approach can determine the optimal starting point (OSP) of the time series and then enhance the prediction performances of the base forecasting models. To illustrate the effectiveness of the proposed approach, comprehensive empirical analysis have been conducted on the M4 dataset and other real world datasets. Empirical results indicate that predictions based on the OSP-TSP approach consistently outperform those using the complete time series dataset. Moreover, comparison results reveals that combining our approach with existing forecasting models can achieve better prediction accuracy, which also reflect the advantages of the proposed approach.
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