# Probabilistic Time Series Forecasting with Structured Shape and Temporal Diversity

arXiv: https://arxiv.org/abs/2010.07349 · HTML: https://mlsanity.com/paper/2010.07349/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Oct 2020.

## Built on by (most cited first, 9 of 9)

- [AdaRNN: Adaptive Learning and Forecasting of Time Series](https://mlsanity.com/paper/2108.04443/) (2021)
- [Deep Time Series Forecasting with Shape and Temporal Criteria](https://mlsanity.com/paper/2104.04610/) (2021)
- [Combating Distribution Shift for Accurate Time Series Forecasting via Hypernetworks](https://mlsanity.com/paper/2202.10808/) (2022)
- [Deep Personalized Glucose Level Forecasting Using Attention-based Recurrent Neural Networks](https://mlsanity.com/paper/2106.00884/) (2021)
- [Diverse Probabilistic Trajectory Forecasting with Admissibility Constraints](https://mlsanity.com/paper/2302.03462/) (2023)
- [Towards Learning in Grey Spatiotemporal Systems: A Prophet to Non-consecutive Spatiotemporal Dynamics](https://mlsanity.com/paper/2208.08878/) (2022)
- [F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data](https://mlsanity.com/paper/2406.16221/) (2024)
- [Coherent Probabilistic Aggregate Queries on Long-horizon Forecasts](https://mlsanity.com/paper/2111.03394/) (2021)
- [Improving the Transferability of Time Series Forecasting with Decomposition Adaptation](https://mlsanity.com/paper/2307.00066/) (2023)

## Abstract (arXiv)

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.