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.

翻译：近年来，基于轻量级MLP的模型通过捕捉稳定的趋势和季节性模式，在时间序列预测中取得了优异的性能。然而，其有效性依赖于局部平稳性的隐含假设，这使得它们在预测高度非平稳序列（尤其是发生突发波动时）的长期预测中容易产生误差，这在网络流量监测等领域是一个常见的挑战。为克服这一局限，我们提出了TimeCatcher，一种新颖的波动感知变分预测框架。TimeCatcher通过一个变分编码器扩展了线性架构，以捕捉历史数据中隐藏的潜在动态模式，并采用一个波动感知增强机制来检测并放大显著的局部变化。在来自交通、金融、能源和气象领域的九个真实世界数据集上的实验表明，TimeCatcher始终优于最先进的基线模型，在以高波动性和突发波动为特征的长期预测场景中改进尤为显著。我们的代码可在 https://github.com/ColaPrinceCHEN/TimeCatcher 获取。