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

• URL: http://arxiv.org/abs/2110.14343v1
• Date: Wed, 27 Oct 2021 10:50:40 GMT
• Title: Comprehensive learning particle swarm optimization enabled modeling framework for multi-step-ahead influenza prediction
• Authors: Siyue Yang, Yukun Bao
• Abstract summary: This study proposes Comprehensive Learning Particle Swarm Optimization based Machine Learning 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

Related papers

• MGCP: A Multi-Grained Correlation based Prediction Network for Multivariate Time Series [54.91026286579748]
  We propose a Multi-Grained Correlations-based Prediction Network. It simultaneously considers correlations at three levels to enhance prediction performance. It employs adversarial training with an attention mechanism-based predictor and conditional discriminator to optimize prediction results at coarse-grained level.
  arXiv  Detail & Related papers  (2024-05-30T03:32:44Z)
• Performative Time-Series Forecasting [71.18553214204978]
  We formalize performative time-series forecasting (PeTS) from a machine-learning perspective. We propose a novel approach, Feature Performative-Shifting (FPS), which leverages the concept of delayed response to anticipate distribution shifts. We conduct comprehensive experiments using multiple time-series models on COVID-19 and traffic forecasting tasks.
  arXiv  Detail & Related papers  (2023-10-09T18:34:29Z)
• Forecast reconciliation for vaccine supply chain optimization [61.13962963550403]
  Vaccine supply chain optimization can benefit from hierarchical time series forecasting. Forecasts of different hierarchy levels become incoherent when higher levels do not match the sum of the lower levels forecasts. We tackle the vaccine sale forecasting problem by modeling sales data from GSK between 2010 and 2021 as a hierarchical time series.
  arXiv  Detail & Related papers  (2023-05-02T14:34:34Z)
• Time Associated Meta Learning for Clinical Prediction [78.99422473394029]
  We propose a novel time associated meta learning (TAML) method to make effective predictions at multiple future time points. To address the sparsity problem after task splitting, TAML employs a temporal information sharing strategy to augment the number of positive samples. We demonstrate the effectiveness of TAML on multiple clinical datasets, where it consistently outperforms a range of strong baselines.
  arXiv  Detail & Related papers  (2023-03-05T03:54:54Z)
• Multivariate Empirical Mode Decomposition based Hybrid Model for Day-ahead Peak Load Forecasting [0.0]
  This study proposed a novel hybrid predictive model built upon multivariate empirical mode decomposition (MEMD) and support vector regression (SVR) Two real-world load data sets from the New South Wales (NSW) and the Victoria (VIC) in Australia have been considered to verify the superiority of the proposed MEMD-PSO-SVR hybrid model.
  arXiv  Detail & Related papers  (2021-10-28T09:42:37Z)
• Comparison of Traditional and Hybrid Time Series Models for Forecasting COVID-19 Cases [0.5849513679510832]
  The coronavirus outbreak of December 2019 has already infected millions all over the world and continues to spread on. Just when the curve of the outbreak had started to flatten, many countries have again started to witness a rise in cases. A thorough analysis of time-series forecasting models is therefore required to equip state authorities and health officials with immediate strategies for future times.
  arXiv  Detail & Related papers  (2021-05-05T14:56:27Z)
• Comparative Analysis of Machine Learning Approaches to Analyze and Predict the Covid-19 Outbreak [10.307715136465056]
  We present a comparative analysis of various machine learning (ML) approaches in predicting the COVID-19 outbreak in the epidemiological domain. The results reveal the advantages of ML algorithms for supporting decision making of evolving short term policies.
  arXiv  Detail & Related papers  (2021-02-11T11:57:33Z)
• STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization [76.57716281104938]
  We develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic.
  arXiv  Detail & Related papers  (2020-12-08T21:21:47Z)
• An Optimal Control Approach to Learning in SIDARTHE Epidemic model [67.22168759751541]
  We propose a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data. We forecast the epidemic evolution in Italy and France.
  arXiv  Detail & Related papers  (2020-10-28T10:58:59Z)
• Data-driven Simulation and Optimization for Covid-19 Exit Strategies [16.31545249131776]
  The rapid spread of the Coronavirus SARS-2 is a major challenge that led almost all governments worldwide to take drastic measures to respond to the tragedy. We have built a pandemic simulation and forecasting toolkit that combines a deep learning estimation of the epidemiological parameters of the disease.
  arXiv  Detail & Related papers  (2020-06-12T11:18:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.