基于分解时频增强的少样本时间序列预测OA

As a pivotal technique for predicting future temporal variations through historical data analysis,time-series forecasting is critical in scientific and engineering domains such as energy management,traffic-flow prediction,finan-cial market-price analysis,and meteorological simulation.Whereas the integration of deep learning has considerably en-hanced the accuracy of predictive models,their high reliance on large-scale annotated data continues to impose signifi-cant constraints on real-world applications,particularly under few-shot scenarios.Although few-shot learning for vision and language modalities has progressed significantly,the methodological framework for few-shot time-series forecast-ing remains underdeveloped.The unique temporal-domain characteristics(trend patterns)and frequency-domain fea-tures(seasonality)inherent in time-series data render it challenging to directly transfer few-shot learning strategies from other modalities.Hence,we propose a temporal-frequency domain data-augmentation framework for few-shot time-series forecasting,which enhances data diversity through decoupling and reinforcing trend-seasonal components.First,we employ time-series decomposition to decouple raw sequences into trend and seasonal components.Second,we de-sign a temporal mix-up strategy to apply linear interpolation perturbations on trend components,coupled with a dominant-shuffle method that injects controllable noise into the spectral domain of seasonal components.Finally,we construct three augmented samples through component recombination,i.e.,time-augmented,frequency-augmented,and hybrid time-frequency-augmented samples.Extensive experiments on four benchmark datasets demonstrate that our method significantly improves few-shot performance across state-of-the-art forecasting models.Compared with large-language-model-based baselines,our approach enables lightweight models to achieve superior prediction accuracy in 80%of scenarios while substantially reducing computational costs.