Dual-Splitting Conformal Prediction for Multi-Step Time Series Forecasting

• URL: http://arxiv.org/abs/2503.21251v1
• Date: Thu, 27 Mar 2025 08:17:18 GMT
• Title: Dual-Splitting Conformal Prediction for Multi-Step Time Series Forecasting
• Authors: Qingdi Yu, Zhiwei Cao, Ruihang Wang, Zhen Yang, Lijun Deng, Min Hu, Yong Luo, Xin Zhou,
• Abstract summary: Time series forecasting is crucial for applications like resource scheduling and risk management, where multi-step predictions provide a comprehensive view of future trends.<n>Most variants of Conformal Prediction (CP) are designed for single-step predictions and face challenges in multi-step scenarios.<n>We propose a novel CP approach designed to capture inherent dependencies within time-series data for multi-step forecasting.
• Score: 16.432179549126236
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time series forecasting is crucial for applications like resource scheduling and risk management, where multi-step predictions provide a comprehensive view of future trends. Uncertainty Quantification (UQ) is a mainstream approach for addressing forecasting uncertainties, with Conformal Prediction (CP) gaining attention due to its model-agnostic nature and statistical guarantees. However, most variants of CP are designed for single-step predictions and face challenges in multi-step scenarios, such as reliance on real-time data and limited scalability. This highlights the need for CP methods specifically tailored to multi-step forecasting. We propose the Dual-Splitting Conformal Prediction (DSCP) method, a novel CP approach designed to capture inherent dependencies within time-series data for multi-step forecasting. Experimental results on real-world datasets from four different domains demonstrate that the proposed DSCP significantly outperforms existing CP variants in terms of the Winkler Score, achieving a performance improvement of up to 23.59% compared to state-of-the-art methods. Furthermore, we deployed the DSCP approach for renewable energy generation and IT load forecasting in power management of a real-world trajectory-based application, achieving an 11.25% reduction in carbon emissions through predictive optimization of data center operations and controls.

Related papers

• PFformer: A Position-Free Transformer Variant for Extreme-Adaptive Multivariate Time Series Forecasting [9.511600544581425]
  PFformer is a position-free Transformer-based model designed for single-target MTS forecasting. PFformer integrates two novel embedding strategies: Enhanced Feature-based Embedding (EFE) and Auto-Encoder-based Embedding (AEE)
  arXiv  Detail & Related papers  (2025-02-27T22:21:27Z)
• Distributed Conformal Prediction via Message Passing [33.306901198295016]
  Conformal Prediction provides distribution-free statistical coverage guarantees for prediction sets by leveraging held-out datasets.<n>We propose two message-passing-based approaches for achieving reliable inference via CP: quantile-based distributed conformal prediction (Q-DCP) and histogram-based distributed conformal prediction (H-DCP)
  arXiv  Detail & Related papers  (2025-01-24T14:47:42Z)
• Loss Shaping Constraints for Long-Term Time Series Forecasting [79.3533114027664]
  We present a Constrained Learning approach for long-term time series forecasting that respects a user-defined upper bound on the loss at each time-step. We propose a practical Primal-Dual algorithm to tackle it, and aims to demonstrate that it exhibits competitive average performance in time series benchmarks, while shaping the errors across the predicted window.
  arXiv  Detail & Related papers  (2024-02-14T18:20:44Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2023-10-17T20:30:16Z)
• QBSD: Quartile-Based Seasonality Decomposition for Cost-Effective RAN KPI Forecasting [0.18416014644193066]
  We introduce QBSD, a live single-step forecasting approach tailored to optimize the trade-off between accuracy and computational complexity. QBSD has shown significant success with our real network RAN datasets of over several thousand cells. Results demonstrate that the proposed method excels in runtime efficiency compared to the leading algorithms available.
  arXiv  Detail & Related papers  (2023-06-09T15:59:27Z)
• IPCC-TP: Utilizing Incremental Pearson Correlation Coefficient for Joint Multi-Agent Trajectory Prediction [73.25645602768158]
  IPCC-TP is a novel relevance-aware module based on Incremental Pearson Correlation Coefficient to improve multi-agent interaction modeling. Our module can be conveniently embedded into existing multi-agent prediction methods to extend original motion distribution decoders.
  arXiv  Detail & Related papers  (2023-03-01T15:16:56Z)
• Copula Conformal Prediction for Multi-step Time Series Forecasting [18.298634240183862]
  We propose a Copula Conformal Prediction algorithm for time series forecasting, CopulaCPTS. We show that CopulaCPTS produces more calibrated and sharp confidence intervals for multi-step prediction tasks than existing techniques.
  arXiv  Detail & Related papers  (2022-12-06T19:32:06Z)
• A novel Deep Learning approach for one-step Conformal Prediction approximation [0.7646713951724009]
  Conformal Prediction (CP) is a versatile solution that guarantees a maximum error rate given minimal constraints. We propose a novel conformal loss function that approximates the traditionally two-step CP approach in a single step.
  arXiv  Detail & Related papers  (2022-07-25T17:46:09Z)
• Collaborative Uncertainty Benefits Multi-Agent Multi-Modal Trajectory Forecasting [61.02295959343446]
  This work first proposes a novel concept, collaborative uncertainty (CU), which models the uncertainty resulting from interaction modules.<n>We build a general CU-aware regression framework with an original permutation-equivariant uncertainty estimator to do both tasks of regression and uncertainty estimation.<n>We apply the proposed framework to current SOTA multi-agent trajectory forecasting systems as a plugin module.
  arXiv  Detail & Related papers  (2022-07-11T21:17:41Z)
• When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting [69.30930115236228]
  Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distributions. We propose PROFHiT, a fully probabilistic hierarchical forecasting model that jointly models forecast distribution of entire hierarchy.
  arXiv  Detail & Related papers  (2022-06-16T06:13:53Z)
• Long Range Probabilistic Forecasting in Time-Series using High Order Statistics [19.12411040726229]
  We present a novel probabilistic forecasting method that produces forecasts coherent in terms of base level and predicted aggregate statistics. We show that our method improves forecast performance across both base level and unseen aggregates post inference on real datasets ranging three diverse domains.
  arXiv  Detail & Related papers  (2021-11-05T11:10:35Z)
• Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression [51.770998056563094]
  Probabilistic Gradient Boosting Machines (PGBM) is a method to create probabilistic predictions with a single ensemble of decision trees. We empirically demonstrate the advantages of PGBM compared to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-06-03T08:32:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.