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Using ARIMA to Predict the Expansion of Subscriber Data Consumption

arXiv 2404.15095 ↗First posted 23 Apr 2024cs.LGcited by 0

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Abstract (arXiv)

This study discusses how insights retrieved from subscriber data can impact decision-making in telecommunications, focusing on predictive modeling using machine learning techniques such as the ARIMA model. The study explores time series forecasting to predict subscriber usage trends, evaluating the ARIMA model's performance using various metrics. It also compares ARIMA with Convolutional Neural Network (CNN) models, highlighting ARIMA's superiority in accuracy and execution speed. The study suggests future directions for research, including exploring additional forecasting models and considering other factors affecting subscriber data usage.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 58 references that are in the map. First posted on arXiv 23 Apr 2024.

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10 of 58 references.

References · 10

  1. 2022N-HiTS: Neural Hierarchical Interpolation for Time Series Forecastingcited by 146
  2. 2013Communication Efficient Distributed Optimization using an Approximate Newton-type Methodcited by 131
  3. 2016Efficient Distributed Learning with Sparsitycited by 38
  4. 2020Forecasting: theory and practicecited by 36
  5. 2020Adversarial Examples in Deep Learning for Multivariate Time Series Regressioncited by 26
  6. 2013An Introductory Study on Time Series Modeling and Forecastingcited by 23
  7. 2018Quantile Regression Under Memory Constraintcited by 17
  8. 2022Time Series Prediction under Distribution Shift using Differentiable Forgettingcited by 5
  9. 2021Privacy-preserving Federated Learning for Residential Short Term Load Forecastingcited by 4
  10. 2022Predict stock prices with ARIMA and LSTMcited by 3

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