Wyniki wyszukiwania - BazTech

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Demand forecasting in the fashion business — an example of customized nearest neighbour and linear mixed model approaches

Henzel Joanna, Wawrowski Łukasz, Kubina Anna, Sikora Marek, Wróbel Łukasz

Annals of Computer Science and Information Systems

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2022

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Vol. 30

61--65

EN

The fashion industry is characterised by the need to make demand forecasts in advance and for highly volatile products for which we often have no sales history at the time the forecasts are made. For this reason, it is necessary to propose forecast mechanisms that can cope with the given conditions. Such forecasts can be based on expert predictions for generalized product categories. In this case, the task of machine learning forecasting methods would be to divide the aggregate prediction into forecasts for individual products, in each colour and size. In the paper, we present several approaches to this specific task. We present the use of the naive method, custom nearest neighbour approach, parametric linear mixed model and an ensemble approach. Overall, the best results we obtained for the ensemble method. Our research was based on real data from fashion retail.

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Enhancement of COVID-19 symptom-based screening with quality-based classifier optimisation

Kozielski Michał, Henzel Joanna, Tobiasz Joanna, Gruca Aleksandra, Foszner Paweł, Zyla Joanna, Bach Małgorzata, Werner Aleksandra, Jaroszewicz Jerzy, Polańska Joanna, Sikora Marek

Bulletin of the Polish Academy of Sciences. Technical Sciences

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2021

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Vol. 69, nr 4

art. no. e137349

EN

Efforts of the scientific community led to the development of multiple screening approaches for COVID-19 that rely on machine learning methods. However, there is a lack of works showing how to tune the classification models used for such a task and what the tuning effect is in terms of various classification quality measures. Understanding the impact of classifier tuning on the results obtained will allow the users to apply the provided tools consciously. Therefore, using a given screening test they will be able to choose the threshold value characterising the classifier that gives, for example, an acceptable balance between sensitivity and specificity. The presented work introduces the optimisation approach and the resulting classifiers obtained for various quality threshold assumptions. As a result of the research, an online service was created that makes the obtained models available and enables the verification of various solutions for different threshold values on new data.

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Impact of time series clustering on fuel sales prediction results

Henzel Joanna, Sikora Marek, Bularz Jakub

Annals of Computer Science and Information Systems

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2021

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Vol. 26

13--21

EN

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

Henzel Joanna, Sikora Marek

Annals of Computer Science and Information Systems

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2020

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Vol. 21

59--68

EN

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.