Using ARIMA to Predict the Expansion of Subscriber Data Consumption
Fuente:
arXiv
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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911850568876032 |
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| author | Nkongolo, Mike Wa |
| author_facet | Nkongolo, Mike Wa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_15095 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using ARIMA to Predict the Expansion of Subscriber Data Consumption Nkongolo, Mike Wa Machine Learning 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. |
| title | Using ARIMA to Predict the Expansion of Subscriber Data Consumption |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2404.15095 |