Study develops smart contract framework for procurement under demand variability.
problem Operational and economic implications of smart contract adoption under moderate uncertainty.
method Multi-supplier model with endogenized adoption costs, supplier readiness, and inventory penalties; analytical and numerical results.
result Partial adoption strategies support moderate demand variability, while excessive digital investment reduces profitability.
Optimizes profit in targeted marketing across multiple markets with varying marketing expenditures.
problem Maximizing profit in a sequential marketing strategy with multiple markets and varying marketing costs.
method Near-optimal algorithms in an adversarial bandit setting, proving regret bounds for different demand curve types.
result Proved near-optimal regret bounds for the profit-maximization problem in targeted marketing.
RFN models urban mobility demand by separating temporal and spatial variability.
problem Aligning supply and demand in MoD systems for efficient transportation.
method Recurrent flow networks with latent variables and normalizing flows.
result RFN models explicitly disentangle temporal and spatial variability in urban mobility.
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.
Modeling business cycles via collective risk fluctuations in economic agents' risk space.
problem Understanding and predicting business cycles through economic agents' risk dynamics.
method Continuous numerical risk grades for economic agents, modeling collective economic variables and flows as functions of risk coordinates, deriving equations for their evolution.
result Business and credit cycles are explained as fluctuations of collective economic variables and their mean risks in the risk space of economic agents.
We propose a contextual-bandit approach for demand side management by offering price incentives. More precisely, a target mean consumption is set at each round and the mean consumption is modeled as a complex function of the distribution of prices sent and of some contextual variables such as the temperature, weather, …
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.
Study examines how COVID-19 intensified demand variability in U.S. supply chains.
problem The amplification of demand variability (Bullwhip Effect) in supply chains during the pandemic.
method Extensive industry-level data analysis using traditional and advanced empirical techniques.
result COVID-19 significantly amplified the Bullwhip Effect across different U.S. industries.
This research improves LSTM for monthly electricity demand forecasting using pattern-based methods.
problem Forecasting mid-term monthly electricity demand with high accuracy.
method Developed a hybrid LSTM model using x-patterns and exponential smoothing.
result The hybrid model outperformed standard LSTM and classical models.
In this paper, we investigate the significance of choosing an appropriate tessellation strategy for a spatio-temporal taxi demand-supply modeling framework. Our study compares (i) the variable-sized polygon based Voronoi tessellation, and (ii) the fixed-sized grid based Geohash tessellation, using taxi demand-supply GP…
Accurate taxi demand-supply forecasting is a challenging application of ITS (Intelligent Transportation Systems), due to the complex spatial and temporal patterns. We investigate the impact of different spatial partitioning techniques on the prediction performance of an LSTM (Long Short-Term Memory) network, in the con…
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…
Study improves retail demand forecasting by integrating macroeconomic data.
problem Lack of accurate demand forecasting due to incomplete data.
method Enriched time series data with macroeconomic variables; compared regression and machine learning models.
result Improved accuracy in predicting retail demand through comprehensive data integration.
The paper forecasts joint electricity demand across 14 British regions using additive models.
problem Forecasting regional electricity demand with cross-regional dependencies.
method Modified Cholesky parametrisation for multivariate Gaussian model, gradient boosting for model selection.
result The proposed model outperforms non-Gaussian copula-based models in forecasting.
Global oil price is an important factor in determining many economic variables in the world's economy. It is generally modeled as a stochastic process and have been studied through different techniques by comparing the historic time series of demand, supply and the price itself. However, there are many historic events …
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.
New BO method efficiently optimizes high-dimensional functions by automatically selecting variables.
problem Efficiently optimizing functions with high-dimensional domains.
method Exploits variable selection to automatically learn sub-spaces without pre-specified dimensions.
result Empirically validated on synthetic and real problems, demonstrating efficiency.
With developing of computation tools in the last years, data analysis methods to find insightful information are becoming more common among industries and researchers. This paper is the first part of the times series analysis of New England electricity price and demand to find anomaly in the data. In this paper time-se…
New models optimize quotes for automated market makers considering various price dynamics and demand variability.
problem Optimizing quotes for automated market makers in volatile price environments.
method Advanced models incorporating stochastic volatility, jumps, Hawkes processes, and Markov-modulated Poisson processes.
result Optimal quotes can be computed using numerical methods tailored to each model.
New algorithm reduces pricing error by a factor of T^2/3.
problem Optimal pricing under non-Lipschitz demand with unknown jumps and atoms.
method Conservative-Markdown Redirect-UCB Pricing, combining estimation, probing, and redirection.
result Achieves optimal regret of O(T^2/3), matching lower bounds.
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…
Tab2vox converts tabular data into 3D images for improved demand forecasting.
problem Forecasting demand influenced by multi-level causes and large volatility.
method Tab2vox neural architecture search (NAS) model to convert tabular data into 3D voxel images for 3D CNN forecasting.
result 3D CNN forecasting model outperforms existing tabular data techniques.
We consider a stochastic inventory control problem under censored demands, lost sales, and positive lead times. This is a fundamental problem in inventory management, with significant literature establishing near-optimality of a simple class of policies called ``base-stock policies'' for the underlying Markov Decision …
Study on thermodynamic costs of simple linear regression.
problem Understanding thermodynamic costs in machine learning models.
method Approximated thermodynamic lower bounds for exact and stochastic linear regression.
result Derived scaling laws for optimal dataset size based on generalization error.
Paper proposes a new method for demand forecasting in pricing contexts.
problem Demand forecasting in pricing contexts, especially in a profit optimal manner.
method Combines Double Machine Learning for causal inference and transformer-based forecasting models.
result Our method outperforms other forecasting methods in off-policy settings.
Paper develops a fair pricing algorithm for dynamic settings with uncertain demand.
problem Fair pricing in dynamic, uncertain demand scenarios.
method Contextual bandit algorithm with dynamic pricing and demand learning.
result Achieves optimal regret bound with fairness constraints.
New algorithm adapts to unknown demand smoothness for dynamic pricing.
problem Dynamic pricing with unknown Hölder smoothness of demand function.
method Self-similarity condition and adaptive algorithm.
result Adaptive algorithm achieves minimax optimal regret without prior knowledge of smoothness.
Study forecasts monthly electricity demand using pattern similarity-based methods.
problem Forecasting monthly electricity demand accurately.
method Pattern similarity-based forecasting methods (PSFMs) including k-NN, fuzzy, kernel regression, and GRNN.
result Ensemble models outperform individual PSFMs in forecasting accuracy.
Optimizes sample and round complexity in adaptive sampling from multiple distributions.
problem Adaptive sampling from multiple distributions with limited rounds and samples.
method Introduces OODS framework and analyzes tradeoffs between sample and round complexity.
result Achieves near-optimal sample complexity and sub-polynomial round complexity.
The disbalance of Supply and Demand is typically considered as the driving force of the markets. However, the measurement or estimation of Supply and Demand at price different from the execution price is not possible even after the transaction. An approach in which Supply and Demand are always matched, but the rate $I=…
Inventory control with unknown demand distribution is considered, with emphasis placed on the case involving discrete nonperishable items. We focus on an adaptive policy which in every period uses, as much as possible, the optimal newsvendor ordering quantity for the empirical distribution learned up to that period. Th…
In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the metho…
New method for disaggregate electricity demand forecasting at household level.
problem Challenges in forecasting electricity demand at individual household level.
method Additive stacking method for probabilistic disaggregate electricity demand forecasting.
result Improved accuracy in disaggregate electricity demand forecasting.
Big T-Rex solves FDR-controlled sparse regression on laptops with millions of variables.
problem Scalable FDR-controlled variable selection for high-dimensional data.
method Early terminated random experiments with memory-mapping and permutation-based dummy generation.
result Solves FDR-controlled Lasso problems with 5 million variables on a laptop in 30 minutes.
LSI enables joint learning of latent variables and generative models.
problem Joint optimization of latent variables and generative models.
method Developed a principled ELBO objective in continuous time for joint learning.
result LSI learns effective latent representations and generative transformations.
Quantile regression improves urban water demand forecasting.
problem Improving probabilistic urban water demand forecasting.
method Comparing five quantile regression algorithms and their combinations for one-day ahead forecasting.
result Linear boosting algorithm performs best for probabilistic urban water demand forecasting.
Sparse alpha-norm regularization has many data-rich applications in Marketing and Economics. Alpha-norm, in contrast to lasso and ridge regularization, jumps to a sparse solution. This feature is attractive for ultra high-dimensional problems that occur in demand estimation and forecasting. The alpha-norm objective is …
Study finds farmers are willing to pay higher premiums for higher coverage in agricultural insurance.
problem Determining the demand factors and WTP for agricultural insurance.
method Conducted a survey of 200 farmers to analyze the impact of socio-demographic variables and premium on insurance purchase decisions.
result Farmers are willing to pay higher premiums for higher coverage in agricultural insurance.
As Internet-based commerce becomes increasingly widespread, large data sets about the demand for and pricing of a wide variety of products become available. These present exciting new opportunities for empirical economic and business research, but also raise new statistical issues and challenges. In this article, we su…
Model forecasts water demand with probabilistic multi-step-ahead approach.
problem Accurate probabilistic forecasts of water demand for operational control.
method Time series model with Lasso for high-dimensional feature space.
result Accurate, interpretable, and fast computable forecasting model.
Paper presents forecasting models for platelet demand.
problem Managing platelet demand and supply is challenging due to variability and short shelf life.
method Utilized ARIMA, Prophet, lasso regression, and LSTM networks on a clinical dataset.
result Multivariate approaches generally have higher accuracy, but simpler ARIMA can suffice with sufficient data.
Proposes a simple solution to Gini importance bias in random forests.
problem Gini importance measure in random forests is biased and unreliable.
method Computes loss reduction on out-of-bag samples instead of in-bag.
result Solves the misleading/untrustworthy Gini importance issue.
Study examines remittances in Nepal, linking external demand and domestic monetary conditions.
problem Understanding the dynamics of remittances in Nepal's economy.
method Constructed composite indices via PCA for external demand and domestic monetary conditions. Used ARDL, cointegration, DOLS, ECM, and machine learning for analysis.
result Strong positive long-run effect of external demand on remittances, significant negative impact of tighter domestic monetary conditions.
Study on newsvendor problem with censored data, showing how much information is lost.
problem Minimizing costs in a newsvendor problem with limited historical demand data.
method Distributionally robust optimization framework, evaluating policies based on worst-case regret.
result Characterization of information loss due to demand censoring and development of a robust algorithm.
Solves inventory control with unknown demand trend using singular control.
problem Optimally managing inventory with an unknown demand trend.
method Formulates as a stochastic control problem under partial observation, solves equivalent separated problem using transition between formulations, and applies viscosity theory.
result Constructs an optimal control rule and shows bounded Lipschitz continuity of free boundaries.
A framework for the generation of bridge-specific fragility utilizing the capabilities of machine learning and stripe-based approach is presented in this paper. The proposed methodology using random forests helps to generate or update fragility curves for a new set of input parameters with less computational effort and…
In this comment we discuss the problem of reconciling the linear efficiency of price returns with the long-memory of supply and demand. We present new evidence that shows that efficiency is maintained by a liquidity imbalance that co-moves with the imbalance of buyer vs. seller initiated transactions. For example, duri…
Motivated by recent advancements in Deep Reinforcement Learning (RL), we have developed an RL agent to manage the operation of storage devices in a household and is designed to maximize demand-side cost savings. The proposed technique is data-driven, and the RL agent learns from scratch how to efficiently use the energ…