Machine learning, Prophet and XGBoost algorithm: Analysis of Traffic Forecasting in Telecom Networks with time series data

Jain, Garima; Прасад, Рам

doi:10.1109/icrito48877.2020.9197864

Cited by 16 publications

(7 citation statements)

References 14 publications

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“…In the dynamic landscape of telecom, machine learning algorithms such as Prophet and XGBoost emerge as pivotal tools for forecasting network traffic [6]. The industry witnesses cost and time reductions through automated test case generation, a testament to the efficiency brought by machine learning [7].…”

Section: Releated Workmentioning

confidence: 99%

Comparative Analysis of XAI Techniques on Telecom Churn Prediction Using SHAP and Interpreted ML Partial Dependence

ÖZKURT

2024

Preprint

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This paper conducts a comparative analysis of two prominent Explainable Artificial Intelligence (XAI) techniques, SHAP (SHapley Additive exPlanations) and Interpreted ML Partial Dependence, applied to a Telecom Churn dataset. The objective is to assess and contrast these techniques in enhancing transparency and interpretability of machine learning models, specifically in telecom churn prediction. The study emphasizes the significance of XAI in ensuring trust and comprehension in predictive modeling. The methodology outlines dataset preprocessing and model training steps. Two separate analyses using SHAP and Interpreted ML Partial Dependence are conducted to evaluate their effectiveness in elucidating model decisions and uncovering feature importance. Results, strengths, and limitations of both techniques are discussed, providing valuable insights into interpretability and robustness. The comparative analysis contributes to understanding XAI methods, emphasizing the importance of selecting appropriate techniques based on context and goals in enhancing transparency in telecom churn prediction models.

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Section: Releated Workmentioning

confidence: 99%

Comparative Analysis of XAI Techniques on Telecom Churn Prediction Using SHAP and Interpreted ML Partial Dependence

ÖZKURT

2024

Preprint

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“…XGBoost (Extreme Gradient Boosting) is an ensemble algorithm that has proven to be effective in various business contexts. The strength of XGBoost lies in its ability to model the relationship between attributes and the target variable in an adaptive and robust manner (Gupta, Yadav, Jha, & Pathak, 2022;Jain & Prasad, 2020). This formula can be seen in Equation 1.…”

Section: A Xgboost Algorithmmentioning

confidence: 99%

Wholesale Inventory Management Optimization: Methodological Approach with XGBoost, SVR, and Random Forest Algorithms

Hutagalung,

Rosalind,

Tuhu

et al. 2023

Brilliance

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This research aims to optimize wholesale inventory management at PT Primafood International Pasir Putih 2 by implementing leading algorithms, namely XGBoost, Support Vector Regression (SVR), and Random Forest. In the wholesale industry, effective inventory management plays a crucial role in maintaining smooth production processes and enhancing company profitability. Despite the acknowledged benefits of inventory management, there are aspects that remain not fully disclosed, particularly concerning demand uncertainty and market fluctuations. This study addresses these gaps by exploring the potential of these three algorithms. Experimental methods with a quantitative approach were employed to shape and prepare the dataset. The analysis and predictions' results using XGBoost, SVR, and Random Forest were evaluated using metrics such as Mean Squared Error (MSE), F1-Score, and Accuracy. The evaluation indicates that XGBoost and SVR exhibit optimal performance with low MSE values of 7714.446 and 119.315, high F1-Scores (0.92), and good accuracy levels (0.86 and 0.85), respectively. While Random Forest shows a higher MSE, it still delivers solid performance with an F1-Score of 0.89 and an accuracy rate of 0.81. These findings suggest that all three algorithms can be considered to enhance inventory management performance at PT Primafood International Pasir Putih 2, with significant potential benefits for overall industry development. This research provides valuable insights for decision-making at the business and industrial levels, highlighting the effectiveness of each algorithm in the context of predicting stock level.

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“…The former performs a statistical decomposition using time series values, which, remarkably, takes into account holidays; while the latter uses the best of both worlds: statistical decomposition and neural networks. Both have demonstrated highly reliable time series prediction results in network traffic [29,30] and in other fields [31], with time series that show irregular patterns and non-stationarity.…”

Section: Traffic Prediction Modelsmentioning

confidence: 99%

Anomaly detection at the subnetwork level: traffic classification for machine learning algorithms

Rivas

Leira²,

Aracil

2022

Preprint

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In this paper we propose a traffic classification methodology for subnetwork level anomaly detection using machine learning algorithms. Our proposed techniques allow researchers and practitioners in the field to classify the corresponding traffic multiplex constituents (subnetworks) according to their predictability level beforehand. Subnetworks deemed as predictable yield smaller prediction error than the rest, under well-known machine learning prediction algorithms, such as ANN, GRU and LSTM. Thus, such predictability features makes the former better suited for anomaly detection purposes, that are based on deviations from predicted values, while the rest are prone to produce false positives.

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Machine learning, Prophet and XGBoost algorithm: Analysis of Traffic Forecasting in Telecom Networks with time series data

Cited by 16 publications

References 14 publications

Comparative Analysis of XAI Techniques on Telecom Churn Prediction Using SHAP and Interpreted ML Partial Dependence

Comparative Analysis of XAI Techniques on Telecom Churn Prediction Using SHAP and Interpreted ML Partial Dependence

Wholesale Inventory Management Optimization: Methodological Approach with XGBoost, SVR, and Random Forest Algorithms

Anomaly detection at the subnetwork level: traffic classification for machine learning algorithms

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