Forecasting of commercial sales with large scale Gaussian Processes

This paper argues that there has not been enough discussion in the field of applications of Gaussian Process for the fast moving consumer goods industry. Yet, this technique can be important as it e.g., can provide automatic feature relevance determination and the posterior mean can unlock insights on the data. Significant challenges are the large size and high dimensionality of commercial data at a point of sale. The study reviews approaches in the Gaussian Processes modeling for large data sets, evaluates their performance on commercial sales and shows value of this type of models as a decision-making tool for management.Comment: 1o pages, 5 figure

Why Don't Prices Rise During Periods of Peak Demand? Evidence from Scanner Data

We examine the retail prices and wholesale prices of a large supermarket chain in Chicago over seven and one-half years. We show that prices tend to fall during the seasonal demand peak for a product and that changes in retail margins account for most of those price changes; thus we add to the growing body of evidence that markups are counter-cyclical. The pattern of margin changes that we observe is consistent with loss leader' models such as the Lal and Matutes (1994) model of retailer pricing and advertising competition. Other models of imperfect competition are less consistent with retailer behavior. Manufacturer behavior plays a more limited role in the counter-cyclicality of prices.

Inventory management using data mining : forecasting in retail trade

xiii, 181 leaves : ill. ; 29 cm.Includes abstract.Includes bibliographical references (leaves 176-181).Inventory management, as an important business issue, plays a significant role in promoting business development. This study aims to apply data mining techniques, such as time series clustering and time series prediction techniques, in inventory management. Based on historical business data sets, time series clustering techniques, such as K-Means and Expectation Maximization are used to categorize inventories into reasonable groups. This study then identifies the most effective prediction technique to accurately predict inventory demands for each group. The traditional statistical evaluation metrics, such as Mean Absolute Percentage Error may not always be good indicators in an inventory management system, where the goal is to have as little inventory as possible without ever running out. The thesis proposes a more appropriated evaluation metric based on cost/benefit analysis of inventory forecasts. Results from a simulation program based on the proposed cost/benefit analysis are compared with statistical metrics

Automatic Time Series Forecasting: The forecast Package for R

Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA models. The algorithms are applicable to both seasonal and non-seasonal data, and are compared and illustrated using four real time series. We also briefly describe some of the other functionality available in the forecast package.

Time Series Forecasting for Retail Sales: A Comparative Study of Traditional Econometric Models and a Machine Learning Approach

openThis thesis compares the forecasting performances of FBProphet, Holt Winters' Exponential Smoothing, and Box Jenkins ARIMA models for retail sales in the US. Mean Absolute Percentage Error (MAPE) is used as the evaluation metric for comparing the forecasts in two different scenarios. The study aims to assess if FBProphet outperforms traditional econometric models in terms of forecasting accuracy. The findings shed light on the relative strengths and weaknesses of these models and contribute to improving retail sales forecasting methodologies

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Automatic time series forecasting: the forecast package for R.

Automatic forecasts of large numbers of univariate time series are often needed in business and other contexts. We describe two automatic forecasting algorithms that have been implemented in the forecast package for R. The first is based on innovations state space models that underly exponential smoothing methods. The second is a step-wise algorithm for forecasting with ARIMA models. The algorithms are applicable to both seasonal and non-seasonal data, and are compared and illustrated using four real time series. We also briefly describe some of the other functionality available in the forecast package.ARIMA models; automatic forecasting; exponential smoothing; prediction intervals; state space models; time series, R.