A random forest method for obsolescence forecasting

Grichi, Yosra; Beauregard, Yvan; Dao, Thiên-My

doi:10.1109/ieem.2017.8290163

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…It is a supervised method that can handle both regression (problems with continuous dependent variables) and classification (problems with categorical dependent variables) tasks. The core concept of the method is to integrate many decision trees to decide the final output rather than depending on individual decision trees, which reduces model variance [23]- [26]. Random forest constructs numerous versions of These tree predictions are combined with a majority vote to get the final projection.…”

Section: Random Forest Modelmentioning

confidence: 99%

Random forest model for forecasting vegetable prices: a case study in Nakhon Si Thammarat Province, Thailand

Kaewchada,

Ruang-On,

Kuhapong

et al. 2023

IJECE

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<span lang="EN-US">The objectives of this research were developing a model for forecasting vegetable prices in Nakhon Si Thammarat Province using random forest and comparing the forecast results of different crops. The information used in this paper were monthly climate data and average monthly vegetable prices collected between 2011 – 2020 from Nakhon Si Thammarat meteorological station and Nakhon Si Thammarat Provincial Commercial Office, respectively. We evaluated model performance based on mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE). The experimental results showed that the random forest model was able to predict the prices of vegetables, including pumpkin, eggplant, and lentils with high accuracy with MAPE values of 0.09, 0.07, and 0.15, with RMSE values of 1.82, 1.46, and 2.33, and with MAE values of 3.32, 2.15, and 5.42, respectively. The forecast model derived from this research can be beneficial for vegetable planting planning in the Pak Phanang River Basin of Nakhon Si Thammarat Province, Thailand.</span>

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Section: Random Forest Modelmentioning

confidence: 99%

Random forest model for forecasting vegetable prices: a case study in Nakhon Si Thammarat Province, Thailand

Kaewchada,

Ruang-On,

Kuhapong

et al. 2023

IJECE

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“…Among them, Jennings et al [ 3 ] tested the effectiveness of random forest (RF), neural network, and support vector machine algorithms for obsolescence risk and life-cycle forecasting. Grichi et al [ 12 ] used the RF method for obsolescence forecasting and improved it by combining it with a genetic algorithm [ 13 ]. The limited success of ML or DL methods for obsolescence prediction can be attributed to the scarcity of available data.…”

Section: Introductionmentioning

confidence: 99%

“…(RF: random forest, NN: neural network, SVM: support vector machine, LR: logistic regression, GB: gradient boosting, GA: genetic algorithm, DT: decision tree, DNN: deep neural network, RNN: recurrent neural network). Category Authors (Year) Application (Target) Method Mathematical Solomon et al (2000) [ 4 ] Integrated circuits (years to obsolescence) Gaussian distribution models Sandborn et al (2007) [ 5 ] Flash memory/DRAM (life cycle curve) Curve fitting Sandborn et al (2011) [ 7 ] Electronic parts (obsolescence dates) Linear regression Ma and Kim (2017) [ 8 ] Flash memory (life cycle curve) Time series model Trabelsi et al (2021) [ 2 ] Smartphones (obsolescence probability density) Statistical test Mastrangelo et al (2021) [ 9 ] Electronic parts (obsolescence probability) conditional probability method Machine Jennings et al (2016) [ 3 ] Cellphones (obsolescence dates) RF/NN/SVM learning Grichi et al (2017) [ 12 ] …”

Section: Introductionmentioning

confidence: 99%

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Adaptive Data Selection-Based Machine Learning Algorithm for Prediction of Component Obsolescence

Moon

Lee

Kim

2022

Sensors

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Product obsolescence occurs in the manufacturing industry as new products with better performance or improved cost-effectiveness are developed. A proactive strategy for predicting component obsolescence can reduce manufacturing losses and lead to customer satisfaction. In this study, we propose a machine learning algorithm for a proactive strategy based on an adaptive data selection method to forecast the obsolescence of electronic diodes. Typical machine learning algorithms construct a single model for a dataset. By contrast, the proposed algorithm first determines a mathematical cover of the dataset via unsupervised clustering and subsequently constructs multiple models, each of which is trained with the data in one cover. For each data point in the test dataset, an optimal model is selected for regression. Results of empirical experiments show that the proposed method improves the obsolescence prediction accuracy and accelerates the training procedure. A novelty of this study is that it demonstrates the effectiveness of unsupervised clustering methods for improving supervised regression algorithms.

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