New framework forecasts both supply and demand in rental markets.
problem Booking models ignore supply, leading to regime-specific ceilings.
method Three-part coupling framework (behavioral, informational, intervention).
result Booking models learn a regime-specific ceiling and become fragile.
This study assesses the influence of the forecast horizon on the forecasting performance of several machine learning techniques. We compare the fo recast accuracy of Support Vector Regression (SVR) to Neural Network (NN) models, using a linear model as a benchmark. We focus on international tourism demand to all sevent…
Bayesian model predicts evolving guest origin markets in tourism.
problem Forecasting the changing composition of guest origin markets in tourism.
method Developed and applied Bayesian Dirichlet autoregressive moving average (BDARMA) models to Airbnb booking data.
result BDARMA models achieve lower forecast error and competitive performance in guest origin market shares.
Bayesian models predict evolving guest origin markets in tourism.
problem Forecasting the changing composition of guest origin markets in tourism.
method Developed and applied Bayesian Dirichlet autoregressive moving average (BDARMA) models to Airbnb booking data.
result BDARMA models outperform standard benchmarks in forecasting guest origin market shares.
Study examines diversification of mid-mountain ski tourism.
problem Understanding transformations in ski mid-mountain territories.
method Applied regional diversification theory to French ski areas.
result Identified three steps in tourism diversification paths.
This study presents an extension of the Gaussian process regression model for multiple-input multiple-output forecasting. This approach allows modelling the cross-dependencies between a given set of input variables and generating a vectorial prediction. Making use of the existing correlations in international tourism d…
Tourism is one of the most important economic activities in the world: for many countries it represents the single largest product in their export basket. However, it is a product difficult to chart: "exporters" of tourism do not ship it abroad, but they welcome importers inside the country. Current research uses socia…
Study analyzes Airbnb booking lead times during global crises using a new metric.
problem Disruptions in booking behaviors during global crises affect forecasting accuracy.
method Normalized L1 (Manhattan) distance to assess lead time divergences.
result Identified two-phase disruption: abrupt change at pandemic onset followed by partial recovery.
Unified framework for integrating linear constraints in time series forecasting.
problem Challenges in traditional time series forecasting algorithms.
method Unified framework combining linear constraints in time series forecasting.
result Exact minimizer of the constrained empirical risk can be computed efficiently using linear algebra.
The article explains Rao distances and conformal mappings for 3D objects.
problem Calculating distances and preserving angles in 3D objects.
method Proposed constructions of distances and angle-preserving mappings.
result Application to virtual tourism and line integrals in complex planes.
PIML uses physics equations in machine learning for better forecasting.
problem Forecasting time series data with physical constraints.
method Physics-informed neural networks (PINNs) and kernel methods.
result PIML improves forecasting accuracy with physical constraints.
Predicting transportation modes from GPS (Global Positioning System) records is a hot topic in the trajectory mining domain. Each GPS record is called a trajectory point and a trajectory is a sequence of these points. Trajectory mining has applications including but not limited to transportation mode detection, tourism…
AI classifies tourist events for better service.
problem Classifying tourist events efficiently across diverse sources.
method CRISP-DM, supervised machine learning, NLP.
result Automatic classification tool for consistent event catalogs.
A new hierarchical forecasting method using machine learning improves forecast accuracy.
problem Improving forecast accuracy in hierarchical forecasting systems.
method Non-linear combination of base forecasts, focusing on both accuracy and coherence.
result The proposed method outperforms existing approaches, especially for diverse series.
Geospatial framework assesses climate risks for California's banking and exposed sectors.
problem Evaluating climate risks on banking and exposed sectors in California.
method Integrates hazard mapping, exposure analysis, and scenario-based financial risk assessment.
result Framework supports portfolio monitoring and institutional readiness under new standards.
Demand variance can result in a mismatch between planned supply and actual demand. Demand shaping strategies such as pricing can be used to shift elastic demand to reduce the imbalance. In this work, we propose to consider elastic demand in the forecasting phase. We present a method to reallocate the historical elastic…
Modeling shared mobility demand considering supply limitations.
problem Inaccurate demand predictions due to limited supply.
method Censored Gaussian Processes for demand modeling.
result Taking supply limitations into account improves demand predictions.
Building on a specific formalization of analogical relationships of the form "A relates to B as C relates to D", we establish a connection between two important subfields of artificial intelligence, namely analogical reasoning and kernel-based machine learning. More specifically, we show that so-called analogical propo…
The paper proposes a new model to better estimate demand from censored data.
problem Challenges in inferring true demand from aggregate, censored data.
method Combines Tobit likelihood with graph diffusion process in Gaussian Processes.
result The new model produces more accurate out-of-sample predictions.
Demand functions for goods are generally cyclical in nature with characteristics such as trend or stochasticity. Most existing demand forecasting techniques in literature are designed to manage and forecast this type of demand functions. However, if the demand function is lumpy in nature, then the general demand foreca…
Google Trends data improves economic forecasts of private consumption.
problem Improving economic forecasts of private consumption.
method Machine learning techniques applied to categorized Google search data.
result Google data can identify patterns to generate a leading indicator in real time.
Study improves cross-modal bike-share and transit demand prediction.
problem Cross-modal ripple effects in urban transportation demand.
method Transfer learning and stacked LSTM models for cross-modal demand prediction.
result Transfer learning models outperform unimodal models in cross-modal demand prediction.
A new metric optimizes forecasts for lumpy, intermittent demand.
problem Inaccurate demand forecasts lead to suboptimal logistics and production.
method Developed a novel metric that considers both statistical and business aspects.
result The new metric yields more accurate predictions for lumpy and intermittent demand.
Paper develops deep models for forecasting intermittent demand.
problem Forecasting intermittent demand with sporadic occurrences.
method Uses deep neural networks to model conditional interdemand time and size distributions.
result Empirical validation of deep models for intermittent demand forecasting.
Statistical arbitrageurs have inelastic demand, contrary to classical models.
problem Understanding the demand elasticity of statistical arbitrageurs.
method Thirteen models from the literature and a quantitative equilibrium model.
result Aggregate demand remains inelastic even with statistical arbitrageurs.
Implementing a set of microeconomic criteria, we develop price dynamics equations using a function of demand/supply with key symmetry properties. The function of demand/supply can be linear or nonlinear. The type of function determines the nature of the tail of the distribution based on the randomness in the supply and…
Recommending the right products is the central problem in recommender systems, but the right products should also be recommended at the right time to meet the demands of users, so as to maximize their values. Users' demands, implying strong purchase intents, can be the most useful way to promote products sales if well …
Novel probabilistic models forecast residential heating and electricity demand at hourly resolution.
problem Accurate hourly forecasting of residential heating and electricity demand.
method Probabilistic deep learning models trained on gas-heated region data.
result Significant improvement in forecast accuracy compared to NREL's ResStock model.
Two neural network models analyze bus system efficiency and demand.
problem Identify service gaps and quantify demand in public transportation.
method Two neural network models considering demographic data and metrics.
result Models can generalize to other cities' bus systems.
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…
Proposes a new model for more accurate demand forecasting considering dynamic contextual information.
problem Traditional methods fail to capture spatio-temporal and dynamic contextual dependencies in demand forecasting.
method Integrates temporal, relational, spatial, and dynamic contextual dependencies using a Context Integrated Graph Neural Network (CIGNN).
result CIGNN outperforms state-of-the-art baselines in multi-step ahead demand forecasting.
The paper tackles ride-hailing fleet repositioning with a calibrated demand approach.
problem Repositioning idle supply before future demand is observed in ride-hailing.
method A predict-then-optimize approach using calibrated demand regimes, a similarity gate, and spatial queue-regret decomposition.
result The spatial gate reduces mean wait time to 82.3s compared to 85.3s for a hand-tuned similarity gate and 85.8s for a distributional-only baseline.
Feature-based time series representations have attracted substantial attention in a wide range of time series analysis methods. Recently, the use of time series features for forecast model averaging has been an emerging research focus in the forecasting community. Nonetheless, most of the existing approaches depend on …
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 …
We consider a firm that sells products over T periods without knowing the demand function. The firm sequentially sets prices to earn revenue and to learn the underlying demand function simultaneously. A natural heuristic for this problem, commonly used in practice, is greedy iterative least squares (GILS). At each ti…
Study on inventory control with changing demand, proposing adaptive algorithms.
problem Inventory control with non-stationary demand distributions.
method Adaptive online algorithms optimizing base-stock policies.
result Sharp separation in adaptability across different inventory models.
Model predicts trading strategies based on latent demand and price impact.
problem Predicting strategic trading behavior of investors with private targets.
method Equilibrium model of dynamic trading, learning, and pricing by strategic investors.
result Trading strategies are a combination of target following, liquidity provision, and front-running based on latent demand and price pressure.
Paper optimizes demand aggregation for low-level electricity markets.
problem Accurate short-term load forecasting at low aggregation levels for market participants.
method Probabilistic portfolio optimization of residential households' demand using ARMA-GARCH models or KDE forecasts.
result Seasonal Residual approach outperforms others in accuracy and efficiency.
MaxCOSD algorithm tackles non-i.i.d. demands and stateful dynamics in online inventory control.
problem Managing inventory with non-i.i.d. demands and stateful dynamics.
method MaxCOSD, an online algorithm with provable guarantees for non-degeneracy assumptions.
result MaxCOSD achieves optimal performance for non-i.i.d. demands and stateful dynamics.
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…
The paper tackles revenue management with time-varying demand using posterior sampling.
problem Maximizing revenue in real-time applications with unknown and time-varying demand.
method Episodic generalization of RM problem, posterior sampling algorithm for linear programming optimization.
result The proposed algorithm outperforms other methods and is comparable to the optimal policy in hindsight.
Unified framework for intermittent demand forecasting using renewal processes.
problem Intermittency in demand forecasting.
method Unified framework based on extensions of discrete-time renewal processes.
result Efficacy demonstrated in forecasting practice with favorable predictive accuracy.
Study shows how China's stock market reflects economic demand changes during COVID-19.
problem Understanding how stock market volatility is influenced by economic demand changes.
method Divided industries into demand-oriented groups and analyzed spillover networks.
result Spillover effects from demand-oriented sectors to consumption-oriented sectors increased during the outbreak.
New framework estimates demand responses across multiple contexts with limited price variation.
problem Estimating heterogeneous linear price-response functions across multiple contexts with limited price variation and confounding.
method Meta-learning framework that identifies conditional mean of task-specific causal demand parameters given a subset of task-specific observables.
result Improved recovery of demand responses relative to standard transfer-learning baselines.
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 …
This paper investigates and compares currency substitution between the currencies of Central and Eastern European (CEE) countries and the euro. In addition, we develop a model with microeconomic foundations, which identifies difference between currency substitution and money demand sensitivity to exchange rate variatio…
Deep-Gap predicts crowdsourcing supply-demand gaps using deep learning.
problem Balancing supply and demand in mobile crowdsourcing.
method Residual learning-based deep neural networks trained on time series data and external factors.
result Deep-Gap achieves lowest forecasting errors compared to state-of-the-art methods.
Study optimizes smart contract adoption under high demand variability using Negative Binomial models.
problem Effective supply chain management under high demand variability.
method Combines dynamic Negative Binomial demand modeling with endogenous smart contract adoption optimization.
result The NB model outperforms other benchmarks in forecasting and optimizing smart contract adoption and order quantity.