A variational Bayesian spatial interaction model for estimating revenue and demand at business facilities

• URL: http://arxiv.org/abs/2108.02594v1
• Date: Thu, 5 Aug 2021 13:03:20 GMT
• Title: A variational Bayesian spatial interaction model for estimating revenue and demand at business facilities
• Authors: Shanaka Perera, Virginia Aglietti, Theodoros Damoulas
• Abstract summary: We study the problem of estimating potential revenue or demand at business facilities and understand its generating mechanism. We develop a Bayesian spatial interaction model, henceforth BSIM, which provides probabilistic predictions about revenues generated by a particular business location. We construct a real-world, large spatial dataset for pub activities in London, UK, which includes over 1,500 pubs and 150,000 customer regions.
• Score: 15.242014520266391
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We study the problem of estimating potential revenue or demand at business facilities and understanding its generating mechanism. This problem arises in different fields such as operation research or urban science, and more generally, it is crucial for businesses' planning and decision making. We develop a Bayesian spatial interaction model, henceforth BSIM, which provides probabilistic predictions about revenues generated by a particular business location provided their features and the potential customers' characteristics in a given region. BSIM explicitly accounts for the competition among the competitive facilities through a probability value determined by evaluating a store-specific Gaussian distribution at a given customer location. We propose a scalable variational inference framework that, while being significantly faster than competing Markov Chain Monte Carlo inference schemes, exhibits comparable performances in terms of parameters identification and uncertainty quantification. We demonstrate the benefits of BSIM in various synthetic settings characterised by an increasing number of stores and customers. Finally, we construct a real-world, large spatial dataset for pub activities in London, UK, which includes over 1,500 pubs and 150,000 customer regions. We demonstrate how BSIM outperforms competing approaches on this large dataset in terms of prediction performances while providing results that are both interpretable and consistent with related indicators observed for the London region.

Related papers

• Bayesian Regression for Predicting Subscription to Bank Term Deposits in Direct Marketing Campaigns [0.0]
  The purpose of this research is to examine the efficacy of logit and probit models in predicting term deposit subscriptions. The target variable was balanced, considering the inherent imbalance in the dataset. The logit model performed better than the probit model in handling this classification problem.
  arXiv  Detail & Related papers  (2024-10-28T21:04:58Z)
• Ranking Policy Learning via Marketplace Expected Value Estimation From Observational Data [8.985446716914515]
  We study the problem of learning a ranking policy for search or recommendation engines in a two-sided e-commerce marketplace. As a value allocation mechanism, the ranking policy allocates retrieved items to the designated slots. We build empirical estimates for the expected reward of the marketplace from observational data.
  arXiv  Detail & Related papers  (2024-10-06T17:53:44Z)
• Uniting contrastive and generative learning for event sequences models [51.547576949425604]
  This study investigates the integration of two self-supervised learning techniques - instance-wise contrastive learning and a generative approach based on restoring masked events in latent space. Experiments conducted on several public datasets, focusing on sequence classification and next-event type prediction, show that the integrated method achieves superior performance compared to individual approaches.
  arXiv  Detail & Related papers  (2024-08-19T13:47:17Z)
• F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data [65.6499834212641]
  We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm. By considering domain similarities through task-specific metadata, our model improved generalization, where the excess risk decreases as the number of training tasks increases. Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
  arXiv  Detail & Related papers  (2024-06-23T21:28:50Z)
• Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference [11.753345219488745]
  We introduce an explainable framework for inferring land use that synergises heterogeneous graph neural networks (HGNs) with Explainable AI techniques. Experiments demonstrate that the proposed HGNs significantly outperform baseline graph neural networks for all six land-use indicators. These analyses demonstrate that the proposed HGNs can suitably support urban stakeholders in their urban planning and policy-making.
  arXiv  Detail & Related papers  (2024-06-19T17:39:10Z)
• A Deep Learning Representation of Spatial Interaction Model for Resilient Spatial Planning of Community Business Clusters [4.8051028509814575]
  We propose a SIM-GAT model to predict visitation flows between community business clusters and their trade areas. A graph-based deep learning model, i.e., Graph AttenTion network (GAT), is used to capture the clusters and interdependencies of business.
  arXiv  Detail & Related papers  (2024-01-09T23:42:21Z)
• Early Churn Prediction from Large Scale User-Product Interaction Time Series [0.0]
  This paper conducts an exhaustive study on predicting user churn using historical data. We aim to create a model forecasting customer churn likelihood, facilitating businesses in comprehending attrition trends and formulating effective retention plans.
  arXiv  Detail & Related papers  (2023-09-25T08:44:32Z)
• Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition [99.7047087527422]
  In this work, we demonstrate that competition can fundamentally alter the behavior of machine learning scaling trends. We find many settings where improving data representation quality decreases the overall predictive accuracy across users. At a conceptual level, our work suggests that favorable scaling trends for individual model-providers need not translate to downstream improvements in social welfare.
  arXiv  Detail & Related papers  (2023-06-26T13:06:34Z)
• Personalized Federated Learning under Mixture of Distributions [98.25444470990107]
  We propose a novel approach to Personalized Federated Learning (PFL), which utilizes Gaussian mixture models (GMM) to fit the input data distributions across diverse clients. FedGMM possesses an additional advantage of adapting to new clients with minimal overhead, and it also enables uncertainty quantification. Empirical evaluations on synthetic and benchmark datasets demonstrate the superior performance of our method in both PFL classification and novel sample detection.
  arXiv  Detail & Related papers  (2023-05-01T20:04:46Z)
• Approximate Bayesian Computation for an Explicit-Duration Hidden Markov Model of COVID-19 Hospital Trajectories [55.786207368853084]
  We address the problem of modeling constrained hospital resources in the midst of the COVID-19 pandemic. For broad applicability, we focus on the common yet challenging scenario where patient-level data for a region of interest are not available. We propose an aggregate count explicit-duration hidden Markov model, nicknamed the ACED-HMM, with an interpretable, compact parameterization.
  arXiv  Detail & Related papers  (2021-04-28T15:32:42Z)
• Entropy as a measure of attractiveness and socioeconomic complexity in Rio de Janeiro metropolitan area [52.77024349608834]
  We use a mobile phone dataset and an entropy-based metric to measure the attractiveness of a location. The results show that the attractiveness of a given location measured by entropy is an important descriptor of the socioeconomic status of the location.
  arXiv  Detail & Related papers  (2020-03-23T15:58:56Z)

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.