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.
IRL approach for studying consumer demand from observed behavior.
problem Confusing observational noise with consumer heterogeneity.
method Developed a Maximum Entropy IRL model for low-dimensional convex optimization.
result Observational noise can be mistaken for consumer heterogeneity.
Study finds consumers are more price-sensitive before livestreams than after.
problem Understanding consumer demand during livestreaming lifecycle.
method Examined consumer demand for live events and recorded versions using data from a livestreaming platform.
result Demand is more price-sensitive before livestreams than after.
New method estimates consumer surplus from randomized pricing data.
problem Estimating consumer surplus from observational data, especially in AI-driven pricing.
method Cumulative Propensity Weights (CPW) and Augmented CPW (ACPW) estimators.
result Validated methods for estimating consumer surplus from randomized pricing data.
Bayesian algorithm learns consumer preferences for energy-saving home automation.
problem Effective energy saving for residential consumers in real-time tariffs.
method Bayesian learning algorithm to estimate comfort level from appliance use history.
result Algorithm outperforms regression analysis in numeric experiments with simulated consumer behavior.
Paper presents a shape-based approach for better household load curve clustering and prediction.
problem Difficulty in classifying and predicting consumer energy consumption due to many clusters.
method Shape-based approach using Dynamic Time Warping (DTW) to align energy consumption patterns.
result Reduces the number of representative groups by 50% and improves prediction accuracy.
Dynamic pricing aims to match power supply and demand in an energy transition.
problem Mismatch between renewable energy supply and consumer demand.
method Formalizes decision-making problem, designs forecasting models, and statistical demand response models.
result Dynamic pricing can synchronise power supply and demand effectively.
CROCS clusters consumer behaviour from smart meters, capturing variability and robustness.
problem Insufficient consumer segmentation in existing clustering methods.
method Two-stage clustering framework: first stage clusters daily load profiles, second stage uses WSMD for set-to-set comparison.
result CROCS captures intra-consumer variability and robustness to anomalies and missing data.
Paper tackles online learning for DR management with incentives.
problem Estimating baseline consumption in DR programs with consumer incentives.
method Online learning scheme using least-squares with perturbed reward prices.
result Achieves low regret of $\mathcal{O}\left((\log{T})^2
ight)$ compared to optimal.
The study finds that real-time prices can lead to higher costs due to consumer flexibility.
problem The integration of consumer flexibility into electricity markets leads to higher costs.
method Agent-based model and multi-agent simulation to study market integration.
result Real-time prices can lead to higher costs due to consumer flexibility and market structure.
New method learns credit prices offline without interaction.
problem Dynamic pricing of consumer credit.
method Offline deep reinforcement learning with Q-Learning.
result Effective personalized pricing policy learned without online interaction.
Predicts next item in sequential bundles using Transformers.
problem Predicting next item in sequentially consumed bundles.
method Used custom Transformers, GPT-3, LSTM, reinforcement learning, Markov models.
result Custom Transformer with decoder-only architecture most accurate.
Model predicts spending behavior of average consumer over short period.
problem Understanding consumer spending dynamics during economic crises.
method Simple hydrodynamical model to describe spending behavior over brief period.
result Model predicts spending behavior of average consumer over short period.
Algorithm learns optimal pricing for diverse consumers with limited stock.
problem Maximizing revenue from personalized pricing with limited inventory.
method Primal-dual learning algorithm that learns dual optimal solution.
result Near-optimal performance with independent regret rate of dimensionality.
We study equilibrium in hedonic markets, when consumers and suppliers have reservation utilities, and the utility functions are separable with respect to price. There is one indivisible good, which comes in different qualities; each consumer buys 0 or 1 unit, and each supplier sells 0 or 1 unit. Consumer types, supplie…
The paper proposes a framework for modeling and analysis of the dynamics of supply, demand, and clearing prices in power system with real-time retail pricing and information asymmetry. Real-time retail pricing is characterized by passing on the real-time wholesale electricity prices to the end consumers, and is shown t…
Paper uses GANs to simulate consumer transactions with SKU constraints.
problem Simulating realistic consumer transactions in retail systems.
method Integrates GANs with consumer behavior and SKU availability constraints.
result Demonstrates enhanced realism in simulated transactions.
LSTMs improve demand forecasting for e-grocery products.
problem Improving demand forecasting accuracy in e-grocery retail.
method Developed and tested univariate and multivariate LSTM models for 100 fast-moving consumer goods.
result LSTMs outperform traditional models for food products, especially in beverage category.
CityTFT models urban building energy using a data-driven approach.
problem Current UBEM methods are time-consuming and based on physics.
method CityTFT uses a TFT framework with an augmented loss function.
result CityTFT predicts energy demands with high accuracy.
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.
Study copyright's impact on creative industries using AI-generated fonts.
problem Estimating supply and demand in creative industries with AI-generated content.
method Neural network embeddings, spatial regression, event-study analyses, structural model of supply and demand.
result Copyright can raise consumer welfare by encouraging product relocation.
There are few papers about the consumption pattern of the Portuguese wine, using econometrics techniques. This work, pretend to analyze the consumers behavior of the wine produced in Portugal, determining the demand equation with panel data methods. There were used statistical data available in the Alentejo Regional Wi…
Consumers with low demand, like households, are generally supplied single-phase power by connecting their service mains to one of the phases of a distribution transformer. The distribution companies face the problem of keeping a record of consumer connectivity to a phase due to uninformed changes that happen. The exact…
Paper uses CVAE to simulate tariff impacts on electricity consumption.
problem Simulating tariff impacts on household electricity consumption.
method CVAE-based clustering and generation of consumption profiles.
result CVAE method can reproduce rebound and side effects in consumption profiles.
The paper analyzes consumer preferences for restaurants using mobile location data.
problem Understanding and predicting consumer choices for restaurants based on location data.
method Bayesian approach with variational inference and stochastic gradient descent.
result The model outperforms standard models in predicting consumer behavior.
Algorithm optimizes dynamic pricing under changing consumer behavior.
problem Optimizing revenue in dynamic pricing with non-stationary consumer demand.
method Multiscale change-point detection algorithm for GLM models.
result Achieves optimal regret bound of O ~ ( s T d T ∧ { V T 1 / 3 d 1 / 3 T 2 / 3 + d T } ) \widetilde{O}(\sqrt{s_TdT}\wedge\{V_T^{1/3}d^{1/3}T^{2/3}+\sqrt{dT}\}) O ( s T d T ∧ { V T 1/3 d 1/3 T 2/3 + d T }) . Proof-of-Stake networks with EIP-1559 exhibit stable token prices and secure network security.
problem Token valuation and institutional capital in Proof-of-Stake networks under EIP-1559
method Open-economy macroeconomic equilibrium model
result Stable steady-state equilibrium price for ETH
Optimal online learning for joint pricing and resource allocation.
problem Maximizing net profit in dynamic pricing and resource allocation with stochastic demand.
method Developed an efficient algorithm using a Lower-Confidence Bound (LCB) meta-strategy over multiple OCO agents.
result Achieved i l d e O ( T m n ) ilde{O}(\sqrt{Tmn}) i l d e O ( T mn ) regret, optimal with respect to time horizon T T T . Study examines retailer responses to stockouts in wholesale environments.
problem Effect of stockouts on future demand in wholesale settings.
method Statistical analysis of historical customer order and delivery data over 4 years.
result Stockouts negatively impact future demand frequency but not value, with effects being short-term.
Enhances demand models with deep learning for personalized pricing.
problem Capturing rich heterogeneity in demand models.
method Integrates deep neural networks into structural models with economic structure.
result Captures rich heterogeneity and creates personalized pricing.
We study continuous time Bertrand oligopolies in which a small number of firms producing similar goods compete with one another by setting prices. We first analyze a static version of this game in order to better understand the strategies played in the dynamic setting. Within the static game, we characterize the Nash e…
The paper develops AI methods to predict weather-sensitive product sales.
problem Lack of accurate demand prediction for weather-sensitive products.
method Artificial Neural Networks applied to historical Walmart sales data.
result Improved demand prediction for weather-sensitive products.
UK imports face higher volatility during high demand periods.
problem Price volatility and national sovereignty during high demand.
method Analysis of early 2017 conditions in France, Germany, and UK.
result National interests take precedence over environmental sustainability.
Proposes a new method for clustering compressed data.
problem Limited communication bandwidth and low-power consumption.
method Joint Variational Autoencoders with Bernoulli mixture models (VAB).
result The model can perform clustering in the compressed data domain.
Regulating crypto and DeFi for inclusive economic advancement.
problem Innovative financial systems pose challenges to traditional regulatory frameworks.
method Formulating regulatory structures that balance innovation and consumer protection.
result Regulatory frameworks are essential for leveraging crypto and DeFi for inclusive economic growth.
This paper proposes a percolation-based model of new-product diffusion in the spirit of Solomon et al. (2000) and Goldenberg et al. (2000). A consumer buys the new product if she has formed her individual valuation of the product (reservation price) and if this valuation is greater or equal than the price of the produc…
New method identifies algo trading strategies as liquidity consumers or providers.
problem Determining if algo trading strategies consume or provide liquidity.
method Analyzes trade and price history to classify strategies as liquidity consumers or providers.
result Identifies net liquidity consumption or provision of algo trading strategies.
Smart grid uses deep learning to optimize household energy use.
problem Optimizing household energy use under real-time pricing schemes.
method Multi-agent deep actor-critic learning for decentralized agents with partial observability.
result Deep reinforcement learning reduces peak-to-average energy consumption and costs.
Enhances U-statistics for semi-supervised datasets using unlabeled data.
problem Efficiently utilizing unlabeled data in semi-supervised settings.
method Semi-supervised U-statistics enhanced by unlabeled data.
result Proposed method is asymptotically Normal and more efficient than classical U-statistics.
Financial institutions have massive computations to carry out overnight which are very demanding in terms of the consumed CPU. The challenge is to price many different products on a cluster-like architecture. We have used the Premia software to valuate the financial derivatives. In this work, we explain how Premia can …
New method improves sales forecasting accuracy using tensor factorization.
problem Improving sales forecasting accuracy in retail businesses.
method Advanced Temporal Latent-factor Approach to Sales forecasting (ATLAS) using tensor factorization.
result Accurate and individualized prediction for sales across multiple stores and products.
Model forecasts natural gas consumption with Fourier series and feedback.
problem Forecast natural gas consumption for risk minimization.
method Modulated Fourier series with temperature deviations, day-ahead feedback.
result Model outperforms time series methods for long-term projections.
LDA identifies latent topics in CFPB consumer complaints over time.
problem Identify latent topics in CFPB consumer complaints for better regulation effectiveness.
method Latent Dirichlet Allocation (LDA) for topic modeling of consumer complaints.
result Time trends of latent topics reveal regulatory effectiveness and consumer protection issues.
Wide Learning automates feature engineering in healthcare datasets.
problem Time-consuming and expert-demanding feature engineering in ML.
method Proposes a Wide Learning architecture for automated feature recommendation.
result Wide Learning reaches 94.38% accuracy in PhysioNet Challenge 2016 dataset.
Consumers adjust their spending based on firms' social stances, influencing firm profits.
problem How consumer spending responds to firms' social stances.
method Using payment card transactions to predict and measure consumer responses to firms' social stances.
result Consumers' spending increases by 19% and decreases by 12% in response to firms' social stances, with effects lasting up to a year.
This study examines how fashion consumption affects self-confidence and buying behavior in Iranian consumers.
problem Understanding the role of self-confidence in fashion buying behavior.
method A questionnaire was used to collect data from 400 consumers in Tehran's clothing market. Structural equations and factor analysis were employed to test the model.
result Interest in fashion, personal taste, utilitarianism, and new products positively impact self-confidence, and self-confidence positively impacts fashion buying behavior.
We consider a market model that consists of financial investors and producers of a commodity. Producers optionally store some production for future sale and go short on forward contracts to hedge the uncertainty of the future commodity price. Financial investors take positions in these contracts in order to diversify t…
Analyzes new economic paradigm for non-independent consumer choices.
problem Non-independent consumer choices due to firm supply and consumer information.
method Develops a new mathematical framework for economic systems.
result New paradigm for economic system description is necessary.