Temporal point processes for purchase categories forecasting

Lysenko, Anton; Shikov, Egor; Bochenina, Klavdiya

doi:10.1016/j.procs.2019.08.201

Cited by 2 publications

(1 citation statement)

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“…Another direction is the prediction of the next purchase timing, which can be viewed combined with the purchase target or separately [5,21]. The approach described in [22] consists of utilizing customer features derived from times and contents of earlier purchases to predict if the customer will make a purchase in a predefined time frame, with features being recalculated each month.…”

Section: Purchase Predictionmentioning

confidence: 99%

Business Purchase Prediction Based on XAI and LSTM Neural Networks

Predić,

Ćirić,

Stoimenov

2023

Electronics

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The black-box nature of neural networks is an obstacle to the adoption of systems based on them, mainly due to a lack of understanding and trust by end users. Providing explanations of the model’s predictions should increase trust in the system and make peculiar decisions easier to examine. In this paper, an architecture of a machine learning time series prediction system for business purchase prediction based on neural networks and enhanced with Explainable artificial intelligence (XAI) techniques is proposed. The architecture is implemented on an example of a system for predicting the following purchases for time series using Long short-term memory (LSTM) neural networks and Shapley additive explanations (SHAP) values. The developed system was evaluated with three different LSTM neural networks for predicting the next purchase day, with the most complex network producing the best results across all metrics. Explanations generated by the XAI module are provided with the prediction results to the user to allow him to understand the system’s decisions. Another benefit of the XAI module is the possibility to experiment with different prediction models and compare input feature effects.

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Section: Purchase Predictionmentioning

confidence: 99%