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Prediction of Electronic Component Prices: from Classical Statistical and Machine Learning Models to Deep Neural Networks with Feature Embedding


The unit price of an electronic component with certain specifications and purchase details could be crucial for the decision-making of customers. With the massive historical purchasing data at Supplyframe, Inc., classical statistical and Machine Learning (ML) models are used to capture the underlined relationship and predict accurate price. The Naive model using mean estimation is adopted as baseline models, followed by the exploration of a wide range of machine learning models including Ordinary Least Squares, Supporting Vector Machine, $k$-Nearest Neighbors, Random Forests (RF), Extreme Gradient Boost (XGB). To make better use of the unstructured features, Deep Neural Networks (DNN) are built based on Convolutional Neural Networks and feature embedding, which maps unstructured features to higher dimension vectors. We observe that the RF and XGB models outperform other classical statistical and ML models when only the structured features are used while the DNN model is proved to be the most powerful by combining both structured and unstructured features. Consistent superior performances are found for the DNN model in terms of the root mean squared error, the prediction interval of the ratios of observed and predicted values, the prediction coverage rate and the capture of the monotonic decreasing relationship between unit prices and purchase quantities.

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