# QBSD: Quartile-Based Seasonality Decomposition for Cost-Effective RAN KPI Forecasting

arXiv: https://arxiv.org/abs/2306.05989 · HTML: https://mlsanity.com/paper/2306.05989/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 48 references that are in the map.
- First posted on arXiv 9 Jun 2023.

## Builds on (most cited first, 14 of 14)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [Recurrent Neural Networks for Time Series Forecasting: Current Status and Future Directions](https://mlsanity.com/paper/1909.00590/) (2019)
- [N-HiTS: Neural Hierarchical Interpolation for Time Series Forecasting](https://mlsanity.com/paper/2201.12886/) (2022)
- [Conditional Time Series Forecasting with Convolutional Neural Networks](https://mlsanity.com/paper/1703.04691/) (2017)
- [Darts: User-Friendly Modern Machine Learning for Time Series](https://mlsanity.com/paper/2110.03224/) (2021)
- [NeuralProphet: Explainable Forecasting at Scale](https://mlsanity.com/paper/2111.15397/) (2021)
- [Parameter Efficient Deep Probabilistic Forecasting](https://mlsanity.com/paper/2112.02905/) (2021)
- [Probabilistic forecasting of heterogeneous consumer transaction-sales time series](https://mlsanity.com/paper/1808.04698/) (2018)
- [Bayesian Median Autoregression for Robust Time Series Forecasting](https://mlsanity.com/paper/2001.01116/) (2020)

## Abstract (arXiv)

Forecasting time series patterns, such as cell key performance indicators (KPIs) of radio access networks (RAN), plays a vital role in enhancing service quality and operational efficiency. State-of-the-art forecasting approaches prioritize accuracy at the expense of computational performance, rendering them less suitable for data-intensive applications encompassing systems with a multitude of time series variables. They also do not capture the effect of dynamic operating ranges that vary with time. To address this issue, we introduce QBSD, a live single-step forecasting approach tailored to optimize the trade-off between accuracy and computational complexity. The method has shown significant success with our real network RAN KPI datasets of over several thousand cells. In this article, we showcase the performance of QBSD in comparison to other forecasting approaches on a dataset we have made publicly available. The results demonstrate that the proposed method excels in runtime efficiency compared to the leading algorithms available while maintaining competitive forecast accuracy that rivals neural forecasting methods.