# Comprehensive learning particle swarm optimization enabled modeling framework for multi-step-ahead influenza prediction

arXiv: https://arxiv.org/abs/2110.14343 · HTML: https://mlsanity.com/paper/2110.14343/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 58 references that are in the map.
- First posted on arXiv 27 Oct 2021.

## Builds on (most cited first, 1 of 1)

- [Multi-Step-Ahead Time Series Prediction using Multiple-Output Support Vector Regression](https://mlsanity.com/paper/1401.2504/) (2014)

## Built on by (most cited first, 3 of 3)

- [CEL: A Continual Learning Model for Disease Outbreak Prediction by Leveraging Domain Adaptation via Elastic Weight Consolidation](https://mlsanity.com/paper/2401.08940/) (2024)
- [Dual-Splitting Conformal Prediction for Multi-Step Time Series Forecasting](https://mlsanity.com/paper/2503.21251/) (2025)
- [A Policy Gradient-Based Sequence-to-Sequence Method for Time Series Prediction](https://mlsanity.com/paper/2406.09643/) (2024)

## Abstract (arXiv)

Epidemics of influenza are major public health concerns. Since influenza prediction always relies on the weekly clinical or laboratory surveillance data, typically the weekly Influenza-like illness (ILI) rate series, accurate multi-step-ahead influenza predictions using ILI series is of great importance, especially, to the potential coming influenza outbreaks. This study proposes Comprehensive Learning Particle Swarm Optimization based Machine Learning (CLPSO-ML) framework incorporating support vector regression (SVR) and multilayer perceptron (MLP) for multi-step-ahead influenza prediction. A comprehensive examination and comparison of the performance and potential of three commonly used multi-step-ahead prediction modeling strategies, including iterated strategy, direct strategy and multiple-input multiple-output (MIMO) strategy, was conducted using the weekly ILI rate series from both the Southern and Northern China. The results show that: (1) The MIMO strategy achieves the best multi-step-ahead prediction, and is potentially more adaptive for longer horizon; (2) The iterated strategy demonstrates special potentials for deriving the least time difference between the occurrence of the predicted peak value and the true peak value of an influenza outbreak; (3) For ILI in the Northern China, SVR model implemented with MIMO strategy performs best, and SVR with iterated strategy also shows remarkable performance especially during outbreak periods; while for ILI in the Southern China, both SVR and MLP models with MIMO strategy have competitive prediction performance