Predicting car sales using maximum entropy principle and proportional growth dynamics.
problem Predicting the sales of new cars over time.
method Analyzing 10 years of Spanish car sales data, applying Gibrat's law, and using the Maximum Entropy Principle.
result Car sales distribution follows predictions from the Maximum Entropy Principle for proportional growth systems.
Statistical models of economic distributions lead to Boltzmann distributions rather than a Pareto power law. This result is supported by two facts: 1. the distributions of income, car sales, marriages or jobs are a matter of chances and luck and not of reason! 2. Data for property, automobile sales, marriages and job m…
We approximate the distribution of total expenditure of a retail company over warranty claims incurred in a fixed period [0, T], say the following quarter. We consider two kinds of warranty policies, namely, the non-renewing free replacement warranty policy and the non-renewing pro-rata warranty policy. Our approximati…
AI-enhanced product embeddings boost demand analysis accuracy.
problem Traditional demand analysis struggles with nuanced product attributes.
method Combining text, images, and tabular data with transformer embeddings for causal inference.
result AI-enhanced embeddings improve sales rank and price predictions.
Consider an ephemeral sale-and-repurchase of a security resulting in the same position before the sale and after the repurchase. A sale-and-repurchase is a wash sale if these transactions result in a loss within ±30 calendar days. Since a portfolio is essentially the same after a wash sale, any tax advantage from …
Paper proposes a machine learning method to predict sale efficacy.
problem Determining the efficacy of online sales from discounts alone.
method Machine learning-based heuristic using Support Vector Machine.
result Predicts sale efficacy with 91.11% accuracy.
To investigate the actual phenomena of transport on a complex network, we analysed empirical data for an inter-firm trading network, which consists of about one million Japanese firms and the sales of these firms (a sale corresponds to the total in-flow into a node). First, we analysed the relationships between sales a…
Study finds sales forecasters overreact to extreme news.
problem Understanding how forecasters react to sales growth news.
method Proposes a framework with fat-tailed dynamics and linear forecasting rule.
result Forecasters overreact to significant sales growth news.
AI-driven sales prioritization boosts renewal bookings by 8.08%.
problem Manual sales account prioritization is inefficient and under-invested.
method Developed an AI-based Account Prioritizer using machine learning and explanation algorithms.
result Generated a +8.08% increase in renewal bookings.
Proposes a mathematical model for safe and scalable self-driving cars.
problem Ensuring safety and scalability in self-driving cars.
method Introduces Responsibility-Sensitive Safety (RSS) model and scalable design.
result Proposes a mathematical model for safety assurance and scalable design.
The paper proposes a ML workflow for B2B sales prediction.
problem Predicting business to business sales outcomes using subjective human evaluations.
method A two-pipeline ML approach on Azure ML, including data enrichment and model training.
result ML predictions improve accuracy and increase sales value.
A new car following model reduces platoon level traffic errors.
problem Enhance traffic micro-simulation accuracy in platoon level traffic.
method Unidirectional interconnected LSTM model structure and scheduled sampling technique.
result The proposed model reduces errors by almost 40% compared to traditional models.
Paper proposes CNN for automatic sales forecast in e-commerce.
problem Challenges in sales forecasting in e-commerce.
method Uses Convolutional Neural Network to automatically extract features from raw log data.
result Experimental validation shows the effectiveness of the proposed method.
This paper improves sales forecasting on Tmall using Fourier decomposition and Tweedie distribution optimization.
problem Sales forecasting for retailers on Tmall.
method Fourier decomposition for seasonality extraction and Tweedie distribution optimization.
result Improved sales forecasting results using optimized models.
A car's motion shows flat parabolic geometry.
problem Understanding the geometry of a car's motion.
method Analyzing a car as a nonholonomic system.
result Shows a car's motion as a flat parabolic geometry.
Sales data in a commodity market (supermarket sales to consumers) has been analysed by studying the fluctuation spectrum and noise correlations. Three related products (ketchup, mayonnaise and curry sauce) have been analysed. Most noise in sales is caused by promotions, but here we focus on the fluctuations in baseline…
Detects ridesourcing cars from ordinary vehicles using transfer learning.
problem Unauthorized ridesourcing activities impairing the industry.
method Two-stage transfer learning framework: RF for initial labeling, CNN for final detection.
result Achieves similar accuracy to supervised learning methods without labeled data.
Dynamic pricing improves sales of low-sale products using online clustering.
problem Low-sale products lack sufficient data for traditional dynamic pricing algorithms.
method Online clustering of product demand and dynamic pricing decisions based on cluster analysis.
result The proposed algorithms significantly outperform traditional single-product pricing policies in increasing revenue.
A kinematics of the motion of a car is reformulated in terms of the theory of gauge potentials (connection on principal bundle). E(2)-connection originates in the no-slipping contact of the car with a road.
Deep RL model mimics human car-following behavior with high accuracy.
problem Developing autonomous cars that can follow other vehicles like humans.
method Deep reinforcement learning using historical driving data and reward function.
result DDPGvRT model achieves lower validation errors (18% spacing, 5% speed) compared to other models.
We analyze a database comprising quarterly sales of 55624 pharmaceutical products commercialized by 3939 pharmaceutical firms in the period 1992--2001. We study the probability density function (PDF) of growth in firms and product sales and find that the width of the PDF of growth decays with the sales as a power law w…
New approach optimizes sales process for B2B businesses.
problem Optimizing the sales process for B2B businesses.
method Causal Predictive Optimization and Generation with three layers: prediction, optimization, and serving.
result Significant wins over legacy systems in LinkedIn implementation.
Short sales allow tax deferral by offsetting gains from ordinary sales.
problem Tax deferral opportunity in short sales not regulated in the Philippine tax system.
method Short selling to offset gains from ordinary sales of identical stocks.
result Tax deferral opportunity exists but is unregulated in the Philippine tax system.
Improved sales forecasting for new products using transfer learning.
problem Insufficient training data for new products leads to inaccurate sales forecasts.
method Network-based Transfer Learning approach for deep neural networks.
result Deep neural networks' prediction accuracy for food sales forecasting can be effectively increased.
DASC combines social media and car sensors to improve disaster response.
problem Inconsistent reliability and inconsistent availability of human sensors.
method Hybrid social-car sensing system using game theory, feedback control, and MDP.
result DASC improves detection accuracy and efficiency in disaster response.
Reduces car control labels to simplify autonomous driving.
problem Redundant labels in semantic maps hinder efficient autonomous driving.
method Quantifies label importance for car control, simplifies labels.
result Reduced labels improve efficiency and simplify autonomous driving tasks.
Article offers models for choosing sale-leaseback vs debt.
problem Choosing between sale-leaseback and debt for commercial real estate.
method Developed decision models for leasing.
result Models can be applied to various types of leasing.
Study compares machine learning and econometric models for demand prediction with and without accounting for sales censorship.
problem Predicting demand based on sales data and correcting bias in estimates of demand parameters.
method Constructed two ensemble models: one accounting for sales censorship and one not. Used censored quantile regression and various machine learning models.
result Machine learning models with censorship accounting provide similar bias-corrected demand sensitivity estimates as econometric models.
Retail company uses Prophet algorithm for accurate sales forecasting.
problem Accurate sales forecasting in the retail industry.
method Facebook's Prophet algorithm and backtesting strategy.
result Framework demonstrates real-world use case capabilities.
Gaussian Processes improve sales forecasting for large retail data.
problem Challenges in forecasting sales with large scale data.
method Large-scale Gaussian Processes for feature relevance and posterior mean analysis.
result Value of Gaussian Processes as a decision-making tool for management.
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.
Proposes a method for training Bayesian neural networks using synthetic data from Raman and CARS spectra.
problem Limited real observations in Raman and CARS spectroscopy.
method Log-Gaussian Gamma Processes and Bayesian Neural Networks.
result Trained Bayesian neural networks provide accurate estimates of Raman and CARS spectra with uncertainty quantification.
ARIMA model outperforms advanced forecasting models in predicting Walmart sales.
problem Forecasting volatile retail sales trends with unknown factors.
method Benchmarked traditional ARIMA model against advanced models like Prophet and LightGBM on historical Walmart sales data.
result ARIMA model outperforms LightGBM and achieves computational efficiency.
The paper analyzes mutual fund advisors' portfolios to improve sales.
problem Improving sales of investment products to suitable advisors.
method Multivariate time series and cosine similarity analysis.
result Recognized patterns in advisors' portfolio interests.
Framework fuses RGB images and depth maps for self-driving car control.
problem Fault tolerance in self-driving cars with sensor failures.
method Deep neural network architecture for sensor fusion.
result Framework can learn to use relevant sensor information even when one fails.
Paper models financial contagion with fire sales and borrowing.
problem Financial contagion and systemic risk in interconnected financial networks.
method Modeling financial contagion in a network with fire sales and borrowing, considering both uncollateralized and collateralized loans.
result Existence and uniqueness of clearing solutions (payments, liquidations, and borrowing) are provided under certain conditions, and these solutions are Nash equilibria.
Improved car-hailing service by analyzing POI effects.
problem Minimize passenger waiting time and optimize vehicle utilization.
method Analyzed POI effects on supply-demand gap and proposed a POI selection scheme integrated with XGBoost.
result More accurate and stable estimation results.
RankNet forecasts car racing positions with improved accuracy and stability.
problem Forecasting rank positions in car racing, especially considering pit stops.
method Cause-effect decomposition in RankNet, incorporating probabilistic forecasting.
result RankNet outperforms baselines significantly, improving MAE by over 10%.
The paper proposes a method to improve sales forecasts by selecting optimal reference classes.
problem Improving forecasts of sales growth exposed to behavioural bias.
method Finding optimal reference classes for each company based on specific predictors and matching forecast distributions to actual sales.
result The past operating margins are strong predictors for future sales distributions.
Keywords: corporate finance, Wrocław University of Economics, net profit margin lub net sales profitability
Detailed empirical studies of publicly traded business firms have established that the standard deviation of annual sales growth rates decreases with increasing firm sales as a power law, and that the sales growth distribution is non-Gaussian with slowly decaying tails. To explain these empirical facts, a theory is dev…
E-commerce sales forecast using LSTM with cross-series information.
problem Accurate sales forecasting in e-commerce with limited univariate methods.
method Global training of LSTM on product assortment hierarchy, incorporating cross-series information.
result LSTM achieves competitive results on Walmart.com dataset, outperforming state-of-the-art techniques.
Model predicts competition between similar products in sales.
problem Predicting cannibalization between similar products in sales.
method Developed a neural network model that computes a 'competitiveness' function based on product features.
result The model outperforms traditional methods in predicting market share.
Empirical data of supermarket sales show stylised facts that are similar to stock markets, with a broad (truncated) Levy distribution of weekly sales differences in the baseline sales [R.D. Groot, Physica A 353 (2005) 501]. To investigate the cause of this, the influence of social interactions and advertisements are st…
Bayesian model predicts sales from ad spending.
problem Predicting sales from advertising expenditures.
method Bayesian structural time series model.
result Model can incorporate prior information and update with data.
Reinforcement learning controls car speed for safe, efficient, and comfortable driving.
problem Safe, efficient, and comfortable car following during autonomous driving.
method Deep reinforcement learning with a reward function for safety, efficiency, and comfort.
result The model reduces dangerous minimum time to collision to 8% of human drivers and maintains efficient headways.
This study improves sales forecasting for Intel Corporation in the semiconductor industry.
problem Accurate sales forecasting in the semiconductor industry for Intel Corporation.
method Innovative incorporation of various indicators into quantitative models, including multiple regressions, time series analysis, random forest, and boosting tree. Ensemble models selected based on validation errors and moving windows validation.
result Development of an ensemble model that captures distinct characteristics at lead time and lines of business levels, improving response to market fluctuations.
The paper introduces a method for forecasting corporate sales growth using multiple reference variables.
problem Forecasting corporate sales growth with multiple reference variables.
method Reference class selection using rank-based algorithms and principal components analysis for data dimension reduction.
result Dimension reduced variables with past sales growth rates and operating margins perform well in forecasting.