Sales-forecasting of Retail Stores using Machine Learning Techniques

Krishna, Akshay; Akhilesh,; Aich, Animikh; Hegde, Chetana

doi:10.1109/csitss.2018.8768765

Cited by 46 publications

(24 citation statements)

References 14 publications

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“…The experiments described in this paper were also based on tabular data, therefore using XGBoost was a justified idea. Additionally, the paper [21] showed that this algorithm has given the best results for salesforecasting of retail stores in their experiments, so it was very likely to give good results also for the problem of forecasting the promotion effect in retail sector. In order to evaluate the models efficiency, the following error measures were used:…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Gradient Boosting Application in Forecasting of Performance Indicators Values for Measuring the Efficiency of Promotions in FMCG Retail

Henzel¹,

Sikora²

2020

Annals of Computer Science and Information Systems

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In the paper, a problem of forecasting promotion efficiency is raised. The authors propose a new approach, using the gradient boosting method for this task. Six performance indicators are introduced to capture the promotion effect. For each of them, within predefined groups of products, a model was trained. A description of using these models for forecasting and optimising promotion efficiency is provided. Data preparation and hyperparameters tuning processes are also described. The experiments were performed for three groups of products from a large grocery company.

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Section: Methodsmentioning

confidence: 99%

“…A hybrid method of k-means algorithm and C4.5 algorithm (decision tree classifier) was shown in [20]. In the paper [21] a comparison of different Machine Learning Techniques was conducted regarding sales-forecasting of retail stores. The authors concluded that boosting algorithms gave better results than the regular regression ones.…”

Section: Related Workmentioning

confidence: 99%

Gradient Boosting Application in Forecasting of Performance Indicators Values for Measuring the Efficiency of Promotions in FMCG Retail

Henzel¹,

Sikora²

2020

Annals of Computer Science and Information Systems

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show abstract

“…In [2] a comparison of various linear and nonlinear models for the sales forecasting task was conducted and the results suggest that non-linear models should be highly considered when dealing with modelling retail sales. In the paper [3] a comparison of different Machine Learning Techniques was conducted regarding sales forecasting of retail stores. The authors concluded that boosting algorithms gave better results than the regular regression ones.…”

Section: Related Workmentioning

confidence: 99%

Impact of time series clustering on fuel sales prediction results

Henzel¹,

Bularz²,

Sikora³

2021

Position and Communication Papers of the 16th Conference on Computer Science and Intelligence Systems

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The purpose of the paper is to check the impact of data clustering in the process of predicting demand. We checked different ways of adding information about similar datasets to the forecasting process and we grouped the measurements in multiple ways. The experiments were executed on 50 time series describing fuels sales (gasoline and diesel sales) on 25 petrol stations from an international company. We described the data preparation process and feature extraction process. In the 9 presented experiments, we used the XGBoost algorithm and some typical time series forecasting methods (ARIMA, moving average). We showed a case study for two datasets and we discussed the practical usage of the tested solutions. The results showed that the solution which used XGBoost model utilising data gathered from all available petrol stations, in general, worked the best and it outperformed more advanced approaches as well as typical time series methods.

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“…In [7] the regression technique and boosting techniques are implemented to process the huge data. This paper tries to predict sales of retail stores using various ML techniques.…”

Section: Related Workmentioning

confidence: 99%

An Interactive Cart with Analytics Recommendation and Tracking-iCART

R¹,

Meeradevi²,

Mundada³

et al. 2020

IJIEEB

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It is very common to use trolleys in supermarkets, they are machines which help us in easily carrying around a lot of items in the supermarket. iCart aims to extend the services offered by these trolleys by augmenting features such as indoor navigation, product recommendation and instantaneous reply to customer queries. For indoor navigation the RSSI values of the bluetooth modules are used to find the customers coordinates and dijkstra's algorithm is used for finding the shortest routes, for product recommendation age, gender and month of the year are passed as input parameters to a classification model and for replying to customer queries a chatbot is implemented using RASA framework. All the features mentioned will be integrated in a single LCD screen mounted on the trolley. This system not only wanes the energy spent by the customer foraging for items, but also increases the owner's profits by providing product recommendations. This model is been implemented using IoT and Machine Learning techniques to save time of customer.

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Sales-forecasting of Retail Stores using Machine Learning Techniques

Cited by 46 publications

References 14 publications

Gradient Boosting Application in Forecasting of Performance Indicators Values for Measuring the Efficiency of Promotions in FMCG Retail

Gradient Boosting Application in Forecasting of Performance Indicators Values for Measuring the Efficiency of Promotions in FMCG Retail

Impact of time series clustering on fuel sales prediction results

An Interactive Cart with Analytics Recommendation and Tracking-iCART

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