A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study improves cross-modal bike-share and transit demand prediction.
A new metric optimizes forecasts for lumpy, intermittent demand.
Heat demand prediction is a prominent research topic in the area of intelligent energy networks. It has been well recognized that periodicity is one of the important characteristics of heat demand. Seasonal-trend decomposition based on LOESS (STL) algorithm can analyze the periodicity of a heat demand series, and decom…
Transport demand is highly dependent on supply, especially for shared transport services where availability is often limited. As observed demand cannot be higher than available supply, historical transport data typically represents a biased, or censored, version of the true underlying demand pattern. Without explicitly…
Study improves dynamic PT fleet optimization under noisy demand predictions.
Many economic applications including optimal pricing and inventory management requires prediction of demand based on sales data and estimation of sales reaction to a price change. There is a wide range of econometric approaches which are used to correct a bias in estimates of demand parameters on censored sales data. T…
This study develops an online predictive optimization framework for dynamically operating a transit service in an area of crowd movements. The proposed framework integrates demand prediction and supply optimization to periodically redesign the service routes based on recently observed demand. To predict demand for the …
The paper proposes a demand prediction model for e-commerce sites using machine learning and stacking.
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons is generally challenging due to the nonlinear and dynamic spatial-temporal dependencies. In this work, we propose to model multi-step citywide passenger deman…
Taxi demand prediction has recently attracted increasing research interest due to its huge potential application in large-scale intelligent transportation systems. However, most of the previous methods only considered the taxi demand prediction in origin regions, but neglected the modeling of the specific situation of …
The study focuses on estimating and predicting time-varying origin to destination (OD) trip tables for a dynamic traffic assignment (DTA) model. A bi-level optimisation problem is formulated and solved to estimate OD flows from pre-existent demand matrix and historical traffic flow counts. The estimated demand is then …
Taxi demand prediction is an important building block to enabling intelligent transportation systems in a smart city. An accurate prediction model can help the city pre-allocate resources to meet travel demand and to reduce empty taxis on streets which waste energy and worsen the traffic congestion. With the increasing…
Proposes a new model to predict travel demand with zero-inflated and long-tail characteristics.
The paper addresses uncertainty in demand prediction for dynamic pricing.
Predicting ambulance demand accurately at a fine resolution in time and space (e.g., every hour and 1 km) is critical for staff / fleet management and dynamic deployment. There are several challenges: though the dataset is typically large-scale, demand per time period and locality is almost always zero. The demand …
Prob-GNN quantifies travel demand uncertainty with deep learning.
This research improves demand forecasting by predicting complete probability density functions using machine learning.
Paper improves bike-sharing demand prediction by adapting to changing patterns.
New model predicts travel demand uncertainty with high accuracy.
Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on pred…
Paper proposes a method for predicting any quantile of short-term electricity demand.
Predicts fine-grained OD matrices for ridesharing platforms to optimize supply-demand balance.
Study improves retail demand forecasting by integrating macroeconomic data.
Study optimizes pricing under uncertainty and capacity constraints.
The paper explains how to predict returns based on firm characteristics.
CONTINA provides adaptive confidence intervals for traffic demand prediction.
The paper tackles ride-hailing fleet repositioning with a calibrated demand approach.
The paper proposes a new model to better estimate demand from censored data.
Develops methods to improve demand counterfactuals from imperfect proxies.
Unified ML approach predicts ED attendances with high accuracy.
Gas demand is made of three components: Residential, Industrial, and Thermoelectric Gas Demand. Herein, the one-day-ahead prediction of each component is studied, using Italian data as a case study. Statistical properties and relationships with temperature are discussed, as a preliminary step for an effective feature s…
One key requirement for effective supply chain management is the quality of its inventory management. Various inventory management methods are typically employed for different types of products based on their demand patterns, product attributes, and supply network. In this paper, our goal is to develop robust demand pr…
RFN models urban mobility demand by separating temporal and spatial variability.
Model predicts trading strategies based on latent demand and price impact.
Bayesian model predicts oncology demand trends with high accuracy.
Modern intelligent transportation systems provide data that allow real-time dynamic demand prediction, which is essential for planning and operations. The main challenge of prediction of dynamic Origin-Destination (O-D) demand matrices is that demands cannot be directly measured by traffic sensors; instead, they have t…
The paper proposes a machine learning technique to optimize prices in fashion e-commerce.
Mobile crowdsourcing has become easier thanks to the widespread of smartphones capable of seamlessly collecting and pushing the desired data to cloud services. However, the success of mobile crowdsourcing relies on balancing the supply and demand by first accurately forecasting spatially and temporally the supply-deman…
CityTFT models urban building energy using a data-driven approach.
Accurately predicting when and where ambulance call-outs occur can reduce response times and ensure the patient receives urgent care sooner. Here we present a novel method for ambulance demand prediction using Gaussian process regression (GPR) in time and geographic space. The method exhibits superior accuracy to MEDIC…
Paper proposes combining GAM and DNN for accurate peak demand estimation from lower-resolution data.
In this paper, we formulate a method for minimising the expectation value of the procurement cost of electricity in two popular spot markets: {\it day-ahead} and {\it intra-day}, under the assumption that expectation value of unit prices and the distributions of prediction errors for the electricity demand traded in tw…
Unified framework for intermittent demand forecasting using renewal processes.
Considering the interdependencies between water and electricity use is critical for ensuring conservation measures are successful in lowering the net water and electricity use in a city. This water-electricity demand nexus will become even more important as cities continue to grow, causing water and electricity utiliti…
The recent wave of AI and automation has been argued to differ from previous General Purpose Technologies (GPTs), in that it may lead to rapid change in occupations' underlying task requirements and persistent technological unemployment. In this paper, we apply a novel methodology of dynamic task shares to a large data…
Proposes a new model for more accurate demand forecasting considering dynamic contextual information.
AutoML improves electricity demand forecasting models.
Predicts next item in sequential bundles using Transformers.