Review for NeurIPS paper: Probabilistic Time Series Forecasting with Shape and Temporal Diversity

Summary and Contributions: In this paper, the authors deal with the time-series forecasting problem, particularly focusing on the probabilistic setting where multiple future outcomes are estimated. In the introduction they clearly present the main drawbacks of methods available in the literature: deep learning-based models are accurate and can capture sharp variations w.r.t. the groundtruth, but they are not able to propose multiple and diverse outcomes for a given input time-series; probabilistic methods can effectively solve the diversity issue but lose the sharpness of the predicted outcomes, and do not have control over the diversity. The authors introduce a method, called STRIPE, to overcome these problems: they use a loss function based on determinantal point processes (DPP) which exploits two kernels (K_shape and K_time) purposefully designed for controlling the shape and temporal diversity; moreover since K_shape and K_time can not be simply added and optimized jointly, the authors introduce an iterative process to model independently the variations in shape and time. Then, they consider the DILATE quality loss and perform an ablation study of various diversity losses, and finally they perform a comparison with state-of-the-art techniques on both synthetic and real world datasets.