提出可感知波动的时序预测框架，提升非平稳序列长期预测准确性
TimeCatcher: A Variational Framework for Volatility-Aware Forecasting of Non-Stationary Time Series
- 用变分编码器捕捉历史数据中的潜在动态模式
- 通过波动感知机制识别并放大局部剧烈变化，显著降低长期预测误差
- 适合高波动场景如网络流量、金融数据的长期预测任务
近期基于轻量级MLP的模型在时序预测中表现优异，能有效捕捉稳定趋势与季节性模式。然而其性能依赖于局部平稳性假设，在高度非平稳序列的长期预测中易出错，尤其在出现突变波动时——这在网页流量监控等领域尤为常见。为此，我们提出TimeCatcher：一种新型波动感知变分预测框架。该框架在线性架构基础上引入变分编码器以挖掘历史数据中的潜在动态模式，并设计波动感知增强机制，用于检测并强化显著的局部波动。在来自流量、金融、能源和气象领域的九个真实数据集上的实验表明，TimeCatcher持续优于当前最优基线，尤其在高波动与突发波动的长期预测场景中表现突出。
原文摘要 · Abstract (English)
Recent lightweight MLP-based models have achieved strong performance in time series forecasting by capturing stable trends and seasonal patterns. However, their effectiveness hinges on an implicit assumption of local stationarity assumption, making them prone to errors in long-term forecasting of highly non-stationary series, especially when abrupt fluctuations occur, a common challenge in domains like web traffic monitoring. To overcome this limitation, we propose TimeCatcher, a novel Volatility-Aware Variational Forecasting framework. TimeCatcher extends linear architectures with a variational encoder to capture latent dynamic patterns hidden in historical data and a volatility-aware enhancement mechanism to detect and amplify significant local variations. Experiments on nine real-world datasets from traffic, financial, energy, and weather domains show that TimeCatcher consistently outperforms state-of-the-art baselines, with particularly large improvements in long-term forecasting scenarios characterized by high volatility and sudden fluctuations. Our code is available at https://github.com/ColaPrinceCHEN/TimeCatcher.
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