# Using ARIMA to Predict the Expansion of Subscriber Data Consumption

arXiv: https://arxiv.org/abs/2404.15095 · HTML: https://mlsanity.com/paper/2404.15095/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 58 references that are in the map.
- First posted on arXiv 23 Apr 2024.

## Builds on (most cited first, 10 of 10)

- [N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting](https://mlsanity.com/paper/2201.12886/) (2022)
- [Communication Efficient Distributed Optimization using an Approximate Newton-type Method](https://mlsanity.com/paper/1312.7853/) (2013)
- [Efficient Distributed Learning with Sparsity](https://mlsanity.com/paper/1605.07991/) (2016)
- [Forecasting: theory and practice](https://mlsanity.com/paper/2012.03854/) (2020)
- [Adversarial Examples in Deep Learning for Multivariate Time Series Regression](https://mlsanity.com/paper/2009.11911/) (2020)
- [An Introductory Study on Time Series Modeling and Forecasting](https://mlsanity.com/paper/1302.6613/) (2013)
- [Quantile Regression Under Memory Constraint](https://mlsanity.com/paper/1810.08264/) (2018)
- [Time Series Prediction under Distribution Shift using Differentiable Forgetting](https://mlsanity.com/paper/2207.11486/) (2022)
- [Privacy-preserving Federated Learning for Residential Short Term Load Forecasting](https://mlsanity.com/paper/2111.09248/) (2021)
- [Predict stock prices with ARIMA and LSTM](https://mlsanity.com/paper/2209.02407/) (2022)

## 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.