Demand Forecasting of Individual Probability Density Functions with Machine Learning

• URL: http://arxiv.org/abs/2009.07052v3
• Date: Thu, 22 Jul 2021 07:12:16 GMT
• Title: Demand Forecasting of Individual Probability Density Functions with Machine Learning
• Authors: F. Wick and U. Kerzel and M. Hahn and M. Wolf and T. Singhal and D. Stemmer and J. Ernst and M. Feindt
• Abstract summary: This work proposes new techniques for assessing the accuracy of predicted distributions. Using the supervised machine learning method "Cyclic Boosting", complete individual probability density functions can be predicted such that each prediction is fully explainable.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Demand forecasting is a central component of the replenishment process for retailers, as it provides crucial input for subsequent decision making like ordering processes. In contrast to point estimates, such as the conditional mean of the underlying probability distribution, or confidence intervals, forecasting complete probability density functions allows to investigate the impact on operational metrics, which are important to define the business strategy, over the full range of the expected demand. Whereas metrics evaluating point estimates are widely used, methods for assessing the accuracy of predicted distributions are rare, and this work proposes new techniques for both qualitative and quantitative evaluation methods. Using the supervised machine learning method "Cyclic Boosting", complete individual probability density functions can be predicted such that each prediction is fully explainable. This is of particular importance for practitioners, as it allows to avoid "black-box" models and understand the contributing factors for each individual prediction. Another crucial aspect in terms of both explainability and generalizability of demand forecasting methods is the limitation of the influence of temporal confounding, which is prevalent in most state of the art approaches.

Related papers

• A Novel Hybrid Approach to Contraceptive Demand Forecasting: Integrating Point Predictions with Probabilistic Distributions [0.8796370521782165]
  We develop a hybrid model that combines point forecasts from domain-specific model with probabilistic distributions from statistical and machine learning approaches. This approach helps address the uncertainties in demand and is particularly useful in resource-limited settings. Our research fills a gap in forecasting contraceptive demand and offers a practical framework that combines algorithmic and human expertise.
  arXiv  Detail & Related papers  (2025-02-13T14:30:11Z)
• Model-agnostic variable importance for predictive uncertainty: an entropy-based approach [1.912429179274357]
  We show how existing methods in explainability can be extended to uncertainty-aware models. We demonstrate the utility of these approaches to understand both the sources of uncertainty and their impact on model performance.
  arXiv  Detail & Related papers  (2023-10-19T15:51:23Z)
• Probabilistic load forecasting with Reservoir Computing [10.214379018902914]
  This work focuses on reservoir computing as the core time series forecasting method. While the RC literature mostly focused on point forecasting, this work explores the compatibility of some popular uncertainty quantification methods with the reservoir setting.
  arXiv  Detail & Related papers  (2023-08-24T15:07:08Z)
• Quantification of Predictive Uncertainty via Inference-Time Sampling [57.749601811982096]
  We propose a post-hoc sampling strategy for estimating predictive uncertainty accounting for data ambiguity. The method can generate different plausible outputs for a given input and does not assume parametric forms of predictive distributions.
  arXiv  Detail & Related papers  (2023-08-03T12:43:21Z)
• Regions of Reliability in the Evaluation of Multivariate Probabilistic Forecasts [73.33395097728128]
  We provide the first systematic finite-sample study of proper scoring rules for time-series forecasting evaluation. We carry out our analysis on a comprehensive synthetic benchmark, specifically designed to test several key discrepancies between ground-truth and forecast distributions.
  arXiv  Detail & Related papers  (2023-04-19T17:38:42Z)
• 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)
• Uncertainty Quantification for Traffic Forecasting: A Unified Approach [21.556559649467328]
  Uncertainty is an essential consideration for time series forecasting tasks. In this work, we focus on quantifying the uncertainty of traffic forecasting. We develop Deep S-Temporal Uncertainty Quantification (STUQ), which can estimate both aleatoric and relational uncertainty.
  arXiv  Detail & Related papers  (2022-08-11T15:21:53Z)
• What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience [63.75363908696257]
  computational reinforcement learning seeks to construct an agent's perception of the world through predictions of future sensations. An open challenge in this line of work is determining from the infinitely many predictions that the agent could possibly make which predictions might best support decision-making. We introduce a meta-gradient descent process by which an agent learns what predictions to make, 2) the estimates for its chosen predictions, and 3) how to use those estimates to generate policies that maximize future reward.
  arXiv  Detail & Related papers  (2022-06-13T21:31:06Z)
• Dense Uncertainty Estimation [62.23555922631451]
  In this paper, we investigate neural networks and uncertainty estimation techniques to achieve both accurate deterministic prediction and reliable uncertainty estimation. We work on two types of uncertainty estimations solutions, namely ensemble based methods and generative model based methods, and explain their pros and cons while using them in fully/semi/weakly-supervised framework.
  arXiv  Detail & Related papers  (2021-10-13T01:23:48Z)
• Quantifying Uncertainty in Deep Spatiotemporal Forecasting [67.77102283276409]
  We describe two types of forecasting problems: regular grid-based and graph-based. We analyze UQ methods from both the Bayesian and the frequentist point view, casting in a unified framework via statistical decision theory. Through extensive experiments on real-world road network traffic, epidemics, and air quality forecasting tasks, we reveal the statistical computational trade-offs for different UQ methods.
  arXiv  Detail & Related papers  (2021-05-25T14:35:46Z)
• Counterfactual Predictions under Runtime Confounding [74.90756694584839]
  We study the counterfactual prediction task in the setting where all relevant factors are captured in the historical data. We propose a doubly-robust procedure for learning counterfactual prediction models in this setting.
  arXiv  Detail & Related papers  (2020-06-30T15:49:05Z)

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.