论文标题
时间序列预测超网络提前生成参数
Time Series Forecasting with Hypernetworks Generating Parameters in Advance
论文作者
论文摘要
预测最近时间序列数据的未来结果并不容易，尤其是当未来数据与过去不同时（即时间序列在时间漂移下）。现有方法显示在数据漂移下的性能有限，我们确定了主要原因：模型需要花费时间来收集足够的训练数据并调整其参数，以便每当基础动态变化时，对复杂的时间模式进行了调整。为了解决这个问题，我们研究了一种新方法；我们建立了一项超网络，而不是调整模型参数（通过不断重新训练模型），而是生成预期在未来数据上表现良好的其他目标模型的参数。因此，我们可以事先调整模型参数（如果超网络正确）。我们使用6个目标模型，6个基线和4个数据集进行了广泛的实验，并表明我们的HyperGPA的表现优于其他基线。
Forecasting future outcomes from recent time series data is not easy, especially when the future data are different from the past (i.e. time series are under temporal drifts). Existing approaches show limited performances under data drifts, and we identify the main reason: It takes time for a model to collect sufficient training data and adjust its parameters for complicated temporal patterns whenever the underlying dynamics change. To address this issue, we study a new approach; instead of adjusting model parameters (by continuously re-training a model on new data), we build a hypernetwork that generates other target models' parameters expected to perform well on the future data. Therefore, we can adjust the model parameters beforehand (if the hypernetwork is correct). We conduct extensive experiments with 6 target models, 6 baselines, and 4 datasets, and show that our HyperGPA outperforms other baselines.
