Summary of Using Arima to Predict the Expansion Of Subscriber Data Consumption, by Mike Wa Nkongolo
Using ARIMA to Predict the Expansion of Subscriber Data Consumption
by Mike Wa Nkongolo
First submitted to arxiv on: 23 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a study on the application of machine learning techniques, specifically the ARIMA model, to predictive modeling of subscriber data trends in telecommunications. Using insights from subscriber data can improve decision-making in this industry. The research focuses on time series forecasting and evaluates the performance of the ARIMA model using various metrics. A comparison with Convolutional Neural Network (CNN) models is also made, 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. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper shows how machine learning can help telecommunications companies make better decisions by predicting what subscribers will do next. They use a special kind of math called ARIMA to forecast trends in subscriber usage. They compare this method with another one called CNN, and find that ARIMA is more accurate and faster. The study also suggests ways for researchers to keep improving their work. |
Keywords
» Artificial intelligence » Cnn » Machine learning » Neural network » Time series
