Conformal Predictive Distributions for Order Fulfillment Time Forecasting

• URL: http://arxiv.org/abs/2505.17340v1
• Date: Thu, 22 May 2025 23:23:52 GMT
• Title: Conformal Predictive Distributions for Order Fulfillment Time Forecasting
• Authors: Tinghan Ye, Amira Hijazi, Pascal Van Hentenryck,
• Abstract summary: This paper introduces a novel framework for distributional forecasting of order fulfillment time.<n>The proposed methods generate competitive distributional forecasts, while machine learning-based point predictions significantly outperform the existing rule-based system.
• Score: 15.378087950770684
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate estimation of order fulfillment time is critical for e-commerce logistics, yet traditional rule-based approaches often fail to capture the inherent uncertainties in delivery operations. This paper introduces a novel framework for distributional forecasting of order fulfillment time, leveraging Conformal Predictive Systems and Cross Venn-Abers Predictors--model-agnostic techniques that provide rigorous coverage or validity guarantees. The proposed machine learning methods integrate granular spatiotemporal features, capturing fulfillment location and carrier performance dynamics to enhance predictive accuracy. Additionally, a cost-sensitive decision rule is developed to convert probabilistic forecasts into reliable point predictions. Experimental evaluation on a large-scale industrial dataset demonstrates that the proposed methods generate competitive distributional forecasts, while machine learning-based point predictions significantly outperform the existing rule-based system--achieving up to 14% higher prediction accuracy and up to 75% improvement in identifying late deliveries.

Related papers

• Diffusion-based Time Series Forecasting for Sewerage Systems [0.3495246564946556]
  We introduce a novel deep learning approach to enhance the accuracy of contextual forecasting in sewerage systems.<n>Our system excels at capturing complex correlations across diverse environmental signals, enabling robust predictions even during extreme weather events.<n>Our empirical tests on real sewerage system data confirm the model's exceptional capability to deliver reliable contextual predictions.
  arXiv  Detail & Related papers  (2025-06-10T08:48:05Z)
• Diffusion-Based Forecasting for Uncertainty-Aware Model Predictive Control [22.60091645818101]
  We propose Diffusion-Informed Model Predictive Control (DI MPC), a generic framework for uncertainty-aware prediction and decision-making in partially observable systems.<n>In our approach, a diffusion-based time series forecasting model is used to probabilistically estimate the evolution of the system's components.<n>We evaluate the framework on the task of energy arbitrage, where a Battery Energy Storage System participates in the day-to-day electricity market of the New York state.
  arXiv  Detail & Related papers  (2025-03-19T10:48:26Z)
• Hybrid Forecasting of Geopolitical Events [71.73737011120103]
  SAGE is a hybrid forecasting system that combines human and machine generated forecasts.<n>The system aggregates human and machine forecasts weighting both for propinquity and based on assessed skill.<n>We show that skilled forecasters who had access to machine-generated forecasts outperformed those who only viewed historical data.
  arXiv  Detail & Related papers  (2024-12-14T22:09:45Z)
• Calibrated Probabilistic Forecasts for Arbitrary Sequences [58.54729945445505]
  Real-world data streams can change unpredictably due to distribution shifts, feedback loops and adversarial actors.<n>We present a forecasting framework ensuring valid uncertainty estimates regardless of how data evolves.
  arXiv  Detail & Related papers  (2024-09-27T21:46:42Z)
• Trustworthy Classification through Rank-Based Conformal Prediction Sets [9.559062601251464]
  We propose a novel conformal prediction method that employs a rank-based score function suitable for classification models. Our approach constructs prediction sets that achieve the desired coverage rate while managing their size. Our contributions include a novel conformal prediction method, theoretical analysis, and empirical evaluation.
  arXiv  Detail & Related papers  (2024-07-05T10:43:41Z)
• Robust Conformal Prediction Using Privileged Information [17.886554223172517]
  We develop a method to generate prediction sets with a guaranteed coverage rate that is robust to corruptions in the training data.<n>Our approach builds on conformal prediction, a powerful framework to construct prediction sets that are valid under the i.i.d assumption.
  arXiv  Detail & Related papers  (2024-06-08T08:56:47Z)
• Enhancing Mean-Reverting Time Series Prediction with Gaussian Processes: Functional and Augmented Data Structures in Financial Forecasting [0.0]
  We explore the application of Gaussian Processes (GPs) for predicting mean-reverting time series with an underlying structure. GPs offer the potential to forecast not just the average prediction but the entire probability distribution over a future trajectory. This is particularly beneficial in financial contexts, where accurate predictions alone may not suffice if incorrect volatility assessments lead to capital losses.
  arXiv  Detail & Related papers  (2024-02-23T06:09:45Z)
• 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)
• Improving Adaptive Conformal Prediction Using Self-Supervised Learning [72.2614468437919]
  We train an auxiliary model with a self-supervised pretext task on top of an existing predictive model and use the self-supervised error as an additional feature to estimate nonconformity scores. We empirically demonstrate the benefit of the additional information using both synthetic and real data on the efficiency (width), deficit, and excess of conformal prediction intervals.
  arXiv  Detail & Related papers  (2023-02-23T18:57:14Z)
• Creating Probabilistic Forecasts from Arbitrary Deterministic Forecasts using Conditional Invertible Neural Networks [0.19573380763700712]
  We use a conditional Invertible Neural Network (cINN) to learn the underlying distribution of the data and then combine the uncertainty from this distribution with an arbitrary deterministic forecast. Our approach enables the simple creation of probabilistic forecasts without complicated statistical loss functions or further assumptions.
  arXiv  Detail & Related papers  (2023-02-03T15:11:39Z)
• 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)
• CovarianceNet: Conditional Generative Model for Correct Covariance Prediction in Human Motion Prediction [71.31516599226606]
  We present a new method to correctly predict the uncertainty associated with the predicted distribution of future trajectories. Our approach, CovariaceNet, is based on a Conditional Generative Model with Gaussian latent variables.
  arXiv  Detail & Related papers  (2021-09-07T09:38:24Z)

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.