论文标题
概率时间序列预测，结构化形状和时间多样性
Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity
论文作者
论文摘要
概率预测在于预测可能未来结果的分布。在本文中，我们解决了非平稳时间序列的这个问题，这非常具有挑战性，但至关重要。我们介绍了基于形状和时间特征来表示结构化多样性的条纹模型，以确保在敏锐而准确的同时进行可能的预测。条纹对预测模型不可知，我们为其配备了依靠确定点过程（DPP）的多元化机制。我们介绍了两个DPP内核，用于建模形状和时间的不同轨迹，它们既可以区分，又被证明是积极的半定义。为了对多样性结构进行明确的控制，我们还设计了一种迭代采样机制，以将潜在空间中的形状和时间表示。在合成数据集上进行的实验表明，条纹明显优于代表多样性的基线方法，同时保持预测模型的准确性。我们还强调了迭代抽样方案的相关性以及使用不同标准来衡量质量和多样性的重要性。最后，对实际数据集进行的实验表明，Stripe能够在最佳样本预测中胜过最先进的概率预测方法。
Probabilistic forecasting consists in predicting a distribution of possible future outcomes. In this paper, we address this problem for non-stationary time series, which is very challenging yet crucially important. We introduce the STRIPE model for representing structured diversity based on shape and time features, ensuring both probable predictions while being sharp and accurate. STRIPE is agnostic to the forecasting model, and we equip it with a diversification mechanism relying on determinantal point processes (DPP). We introduce two DPP kernels for modeling diverse trajectories in terms of shape and time, which are both differentiable and proved to be positive semi-definite. To have an explicit control on the diversity structure, we also design an iterative sampling mechanism to disentangle shape and time representations in the latent space. Experiments carried out on synthetic datasets show that STRIPE significantly outperforms baseline methods for representing diversity, while maintaining accuracy of the forecasting model. We also highlight the relevance of the iterative sampling scheme and the importance to use different criteria for measuring quality and diversity. Finally, experiments on real datasets illustrate that STRIPE is able to outperform state-of-the-art probabilistic forecasting approaches in the best sample prediction.
