An application of support vector machines to sales forecasting under promotions

Pillo, G. Di; Latorre, Vittorio; Lucidi, Stefano; Procacci, Enrico

doi:10.1007/s10288-016-0316-0

Cited by 31 publications

(19 citation statements)

References 18 publications

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“…Neural Networks (Kuo, 2001); Regression Trees (Gür Ali et al, 2009), Gray relation analysis and multilayer functional link networks Ou, 2009, 2011), a two level switching model selecting between a simple moving average and a non-linear predictor (e.g., k-nearest neighbor, decision trees) based on the characteristics of the time series (Žliobaitė, Bakker, and Pechenizkiy, 2012), Support Vector Machines (Gür Ali and Yaman, 2013;Pillo, Latorre, Lucidi, and Procacci, 2016), Wavelets Neural Networks (Veiga, Veiga, Puchalski, Coelho, and Tortato, 2016), and Bayesian P-splines (Lang et al, 2015). An exception where non-linearities led to poor performance is van Donselaar et al (2016) who analyzed the impact of relative price discounts on product sales during a promotion but did not find conclusive evidence for the presence of threshold and/or saturation levels for price discounts for perishable products.…”

Section: Nonlinear and Machine Learning Methodsmentioning

confidence: 99%

Retail forecasting: Research and practice

Fildes

Kolassa

2022

International Journal of Forecasting

174

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This paper first introduces the forecasting problems faced by large retailers, from the strategic to the operational, from the store to the competing channels of distribution as sales are aggregated over products to brands to categories and to the company overall. Aggregated forecasting that supports strategic decisions is discussed on three levels: the aggregate retail sales in a market, in a chain, and in a store. Product level forecasts usually relate to operational decisions where the hierarchy of sales data across time, product and the supply chain is examined. Various characteristics and the influential factors which affect product level retail sales are discussed. The data rich environment at lower product hierarchies makes data pooling an often appropriate strategy to improve forecasts, but success depends on the data characteristics and common factors influencing sales and potential demand. Marketing mix and promotions pose an important challenge, both to the researcher and the practicing forecaster. Online review information too adds further complexity so that forecasters potentially face a dimensionality problem of too many variables and too little data. The paper goes on to examine evidence on the alternative methods used to forecast product sales and their comparative forecasting accuracy. Many of the complex methods proposed have provided very little evidence to convince as to their value, which poses further research questions. In contrast, some ambitious econometric methods have been shown to outperform all the simpler alternatives including those used in practice. New product forecasting methods are examined separately where limited evidence is available as to how effective the various approaches are. The paper concludes with some evidence describing company forecasting practice, offering conclusions as to the research gaps but also the barriers to improved practice.

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Section: Nonlinear and Machine Learning Methodsmentioning

confidence: 99%

Retail forecasting: Research and practice

Fildes

Kolassa

2022

International Journal of Forecasting

174

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“…[12] investigate online review and online promotional strategies such as free delivery and price cut discount combined with sentiment from user reviews. [13] studied sales prediction in large retail distribution. The study uses SVM as forecasting model and compare the performance to exponential smoothing model (ES), and Holt-Winter triple Exponential Smoothing (HWES).…”

Section: Literature Reviewmentioning

confidence: 99%

Promotion Scenario Based Sales Prediction on E-Retail Groceries Using Data Mining

Muqodas¹,

Kusuma²

2021

IJETAE

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Mostly in many business cases, sales prediction plays an important role. Production planning is a good example. One aspect which affecting sales forecasting is promotion schedule. Since using promotion is commonly done nowadays, especially in internet business, it is hardly seen a day without promotion in Indonesian e-commerce. Thus, this study discusses about forecasting future sales based on promotion scenario data with main objective is to discover the best machine learning algorithm and model to forecast future sales. Promotion mechanism which employed in this study are price cut, buy 1-get 1, and product bundling. We use 577 data from January 2018 to July 2019 as dataset. We compare kNN, GLM, and SVR as the model predictor to forecast number of transactions in a day. From the experiment k-NN yielded the highest performance ability with squared correlation of 0.938. the worst model predictor for this case is GLM with squared correlation of 0.507. We also determine the best parameter input for each parameter using grid optimization method. We discover 2 is the best k value of kNN and Manhattan distance is the best distance calculation for this case

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“…In this study, the support vector machine linear model and its nonlinear model were used by combining the rapid basis kernel function where the rapid basis model has a higher accuracy. Di pillo et al [13] in another study explained the use of support vector regression for sales forecasting and used this method in another case study. Table 1 indicates some areas where machine learning methods and data mining are used for sales forecasting.…”

Section: -Review Of Literaturementioning

confidence: 99%

The Development of a Hybrid Error Feedback Model for Sales Forecasting

Foumani¹,

Goudarzi²

2021

JIST

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Sales forecasting is one of the significant issues in the industrial and service sector which can lead to facilitated management decisions and reduce the lost values in case of being dealt with properly. Also sales forecasting is one of the complicated problems in analyzing time series and data mining due to the number of intervening parameters. Various models were presented on this issue and each one found acceptable results. However, developing the methods in this study is still considered by researchers. In this regard, the present study provided a hybrid model with error feedback for sales forecasting. In this study, forecasting was conducted using a supervised learning method. Then, the remaining values (model error) were specified and the error values were forecasted using another learning method. Finally, two trained models were combined together and consecutively used for sales forecasting. In other words, first the forecasting was conducted and then the error rate was determined by the second model. The total forecasting and model error indicated the final forecasting. The computational results obtained from numerical experiments indicated the superiority of the proposed hybrid method performance over the common models in the available literature and reduced the indicators related to forecasting error.

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An application of support vector machines to sales forecasting under promotions

Cited by 31 publications

References 18 publications

Retail forecasting: Research and practice

Retail forecasting: Research and practice

Promotion Scenario Based Sales Prediction on E-Retail Groceries Using Data Mining

The Development of a Hybrid Error Feedback Model for Sales Forecasting

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