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.
Machine learning improves hierarchical forecasting of sales time series.
problem Hierarchical forecasting of sales time series is challenging due to dynamic changes.
method Used artificial neural networks, extreme gradient boosting, and support vector regression to disaggregate time series.
result Machine learning models outperform traditional methods in predicting sales time series with high volatility.
Proposes a GNN for multivariate time-series prediction with filtering.
problem Low signal-to-noise ratio in complex systems data.
method Integrates a spatial-temporal GNN with a matrix filtering module to generate filtered graphs.
result Proposed model outperforms baseline approaches in multivariate time-series prediction.
Study combines dynamic mode and wavelet decomposition for marketing time series analysis.
problem Insufficient quantitative studies in marketing literature.
method Dynamic mode decomposition and wavelet decomposition for marketing time series.
result Effect of time scale on brand sales persistence and forecasting.
Estimates treatment effects in time series data with always-missing controls.
problem Lack of control group in time series data, especially during specific events.
method Recover control group in event period, account for confounders and temporal dependencies.
result Robust estimation of control group's potential outcome and accurate predicted holiday effect.
Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-the-art techniques are typically univariate methods, which produce forecasts considering only the historical sales data of a single product. However, in a situation where large quantities of related time series …
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.
We propose a robust implementation of the Nerlove--Arrow model using a Bayesian structural time series model to explain the relationship between advertising expenditures of a country-wide fast-food franchise network with its weekly sales. Thanks to the flexibility and modularity of the model, it is well suited to gener…
Paper predicts future sales using machine learning techniques.
problem Predict future sales of retail products using past data.
method Used eXtreme Gradient Boosting (XGBoost) and Long Short Term Memory (LSTM) for forecasting.
result XGBoost performed better than LSTM in predicting sales.
This study enhances sales forecasts by integrating market indicators into forecasting models.
problem Traditional forecasting models rely solely on historical demand data.
method Automated integration of macroeconomic time series data (GDP growth) into forecasting models using feature selection methods.
result Feature selection methods, especially Forward Feature Selection, significantly improve forecasting accuracy.
This paper compares forecasting techniques for sales data, focusing on profit-driven models.
problem Choosing the best forecasting technique for sales data is challenging.
method Compares various forecasting methods including ML, statistics, and econometrics.
result Simple seasonal models consistently outperform other methodologies.
Pharmaceutical targeting is one of key inputs for making sales and marketing strategy planning. Targeting list is built on predicting physician's sales potential of certain type of patient. In this paper, we present a time-sensitive targeting framework leveraging time series model to predict patient's disease and treat…
Sales forecasts are crucial for the E-commerce business. State-of-the-art techniques typically apply only univariate methods to make prediction for each series independently. However, due to the short nature of sales times series in E-commerce, univariate methods don't apply well. In this article, we propose a global m…
Time series data in the retail world are particularly rich in terms of dimensionality, and these dimensions can be aggregated in groups or hierarchies. Valuable information is nested in these complex structures, which helps to predict the aggregated time series data. From a portfolio of brands under HUUB's monitoring, …
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.
In mutual fund, an investment adviser gives advice to clients about investing in securities such as stocks, bonds, mutual funds, or exchange traded funds. Some investment advisers manage portfolios of securities. In this paper, we analyze advisor portfolio for each advisor so as to recognize the pattern in each adviser…
Model predicts alternating market dominance for two competing firms.
problem Alternating market dominance of two competing firms in a competitive market.
method Deterministic model with investment strategy, stability analysis of fixed points, bifurcation diagrams, time-series analysis.
result Leapfrogging regime is stabilized by specific parameter values and high elasticity coefficient.
Sales forecasting plays a prominent role in business planning and business strategy. The value and importance of advance information is a cornerstone of planning activity, and a well-set forecast goal can guide sale-force more efficiently. In this paper CPU sales forecasting of Intel Corporation, a multinational semico…
Study evaluates local explanation methods for time series forecasting.
problem Lack of local interpretability methods for multivariate time series forecasting.
method Proposed two novel evaluation metrics: Area Over the Perturbation Curve for Regression and Ablation Percentage Threshold.
result Comprehensive comparison of local explanation models on two datasets.
New algorithm uncovers causal relations in non-stationary time series.
problem Discovering causal relations from non-stationary time series data.
method Constraint-based, non-parametric algorithm for semi-stationary time series.
result Algorithm PCMCIΩ identifies causal graph with CI tests. We tackle anomaly detection in sparse time series data.
problem Sparse time series with low signal-to-noise ratios and non-uniform performance.
method We introduce a novel generative procedure for benchmark datasets and demonstrate how anomaly score smoothing improves performance.
result Anomaly score smoothing consistently improves performance in low-count time series anomaly detection.
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.
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.
A method to improve time series forecasting by dynamically adjusting weights of forecasters.
problem Challenges in time series forecasting due to evolving data distributions.
method Dynamic re-weighting of forecasters based on evolving data distributions.
result Competitive performance compared to state-of-the-art methods for combining forecasters.
Advanced forecasting models outperform Holt-Winters and ARIMA for stock market data.
problem Forecasting stock market data with improved accuracy.
method Developed 24 two-parameter families of forecasting functions using a nonparametric approach.
result Our models outperform Holt-Winters and ARIMA in terms of lower sum of absolute errors and higher number of accurate forecasts.
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…
We analyze an exhaustive data-set of new-cars monthly sales. The set refers to 10 years of Spanish sales of more than 6500 different car model configurations and a total of 10M sold cars, from January 2007 to January 2017. We find that for those model configurations with a monthly market-share higher than 0.1% the sale…
Sales forecast is an essential task in E-commerce and has a crucial impact on making informed business decisions. It can help us to manage the workforce, cash flow and resources such as optimizing the supply chain of manufacturers etc. Sales forecast is a challenging problem in that sales is affected by many factors in…
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…
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.
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.
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 …
A new multi-phase approach improves supply chain forecasting accuracy.
problem Improving forecast accuracy for hierarchical supply chain demands.
method Independent child-level forecasting followed by parent-level estimation.
result 82-90% improvement in forecast accuracy compared to traditional methods.
MARS model outperforms others in stock price prediction across sectors.
problem Developing accurate models for stock price prediction.
method Used time series, econometric, machine learning, and deep learning models on stock data.
result MARS model is the best performing model across IT, Banking, and Health sectors.
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…
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.
New model predicts sales of new products with short life cycles.
problem Forecasting sales of new products with short lead times and life cycles.
method Developed an exponential factorization machine (EFM) to consider attributes and pairwise interactions.
result EFM model outperforms existing models in terms of MAPE and MAE.
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.
The paper proposes blending gradient boosted trees and neural networks for hierarchical time series forecasting.
problem Point and probabilistic forecasting of hierarchical time series.
method A blending methodology of gradient boosted trees and neural networks, with feature engineering and diverse model selection.
result Ranked within the gold medal range in both Accuracy and Uncertainty tracks of the M5 Competition.
Paper introduces SALE for better state-action learning in RL.
problem Challenges in representation learning for low-level states in RL.
method Introduces SALE, a novel approach for learning embeddings of state-action interactions.
result TD7 algorithm significantly outperforms existing continuous control algorithms.
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.
Many retailers today employ inventory management systems based on Re-Order Point Policies, most of which rely on the assumption that all decreases in product inventory levels result from product sales. Unfortunately, it usually happens that small but random quantities of the product get lost, stolen or broken without r…
Study asset price bubbles in markets with short sales prohibitions and model uncertainty.
problem Investigating asset price bubbles in markets with short sales prohibitions and model uncertainty.
method Introducing a novel definition of the fundamental price and analyzing the types and characterization of bubbles using a new fundamental theorem of asset pricing and superhedging duality.
result Two distinct types of bubbles arise depending on the maturity structure of the asset, and conditions for their existence are provided.
Optimizes real estate prices with dynamic strategies.
problem Optimizing prices for limited real estate goods over time.
method Develops a mathematical model considering variable demand, time value, and growth of real estate value.
result Enhanced model for better revenue management in real estate.
The Internet is known to have had a powerful impact on on-line retailer strategies in markets characterised by long-tail distribution of sales. Such retailers can exploit the long tail of the market, since they are effectively without physical limit on the number of choices on offer. Here we examine two extensions of t…
Improved sales forecasting at various levels using ensemble methods.
problem Enhancing sales forecasting accuracy at different levels of e-commerce data.
method Hierarchical robust aggregation of sales forecasts using exponential smoothing and Holt's linear trend method.
result Better forecasts at subsubfamily, subfamily, and family levels compared to individual techniques.
The addition of wealth-attained advantage (WAA) to the Yard-Sale Model (YSM) of asset exchange has been demonstrated to induce wealth condensation. In a model of WAA for which the bias is a continuous function of the wealth difference of the transacting agents, the condensation was shown to arise from a second-order ph…