mlsanityData to 7 Oct 2026

Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics

arXiv 2206.10696 ↗First posted 21 Jun 2022cs.LG nlin.CD q-bio.PEcited by 16

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Abstract (arXiv)

Infectious diseases remain among the top contributors to human illness and death worldwide, among which many diseases produce epidemic waves of infection. The unavailability of specific drugs and ready-to-use vaccines to prevent most of these epidemics makes the situation worse. These force public health officials and policymakers to rely on early warning systems generated by reliable and accurate forecasts of epidemics. Accurate forecasts of epidemics can assist stakeholders in tailoring countermeasures, such as vaccination campaigns, staff scheduling, and resource allocation, to the situation at hand, which could translate to reductions in the impact of a disease. Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics. We analyse a wide variety of epidemic time series datasets using a maximal overlap discrete wavelet transform (MODWT) based autoregressive neural network and call it EWNet model. MODWT techniques effectively characterize non-stationary behavior and seasonal dependencies in the epidemic time series and improve the nonlinear forecasting scheme of the autoregressive neural network in the proposed ensemble wavelet network framework. From a nonlinear time series viewpoint, we explore the asymptotic stationarity of the proposed EWNet model to show the asymptotic behavior of the associated Markov Chain. We also theoretically investigate the effect of learning stability and the choice of hidden neurons in the proposal. From a practical perspective, we compare our proposed EWNet framework with several statistical, machine learning, and deep learning models. Experimental results show that the proposed EWNet is highly competitive compared to the state-of-the-art epidemic forecasting methods.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 10 of its 199 references that are in the map. First posted on arXiv 21 Jun 2022.

Builds on

10 of 199 references.

References · 10

  1. 2022Are Transformers Effective for Time Series Forecasting?cited by 1,400
  2. 2017DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networkscited by 1,004
  3. 2019N-BEATS: Neural basis expansion analysis for interpretable time series forecastingcited by 744
  4. 2021Monash Time Series Forecasting Archivecited by 256
  5. 2020Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Casecited by 129
  6. 2021Darts: User-Friendly Modern Machine Learning for Time Seriescited by 90
  7. 2019Probabilistic Forecasting with Temporal Convolutional Neural Networkcited by 63
  8. 2020Forecasting: theory and practicecited by 36
  9. 2017Phenomenological forecasting of disease incidence using heteroskedastic Gaussian processes: a dengue case studycited by 3
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 2; 2025: 4; 2026: 4.

16 papers.

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  1. 2023Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-artcited by 14
  2. 2022W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecastingcited by 12
  3. 2023Randomized prior wavelet neural operator for uncertainty quantificationcited by 4
  4. 2025Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreakcited by 4
  5. 2026Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemicscited by 4
  6. 2022An ensemble neural network approach to forecast Dengue outbreak based on climatic conditioncited by 3
  7. 2024Spatiotemporal Forecasting of Traffic Flow using Wavelet-based Temporal Attentioncited by 1
  8. 2026EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecastingcited by 1
  9. 2022Knowledge-based Deep Learning for Modeling Chaotic Systemscited by 0
  10. 2023Machine Learning for Infectious Disease Risk Prediction: A Surveycited by 0
  11. 2024WaveCatBoost for Probabilistic Forecasting of Regional Air Quality Datacited by 0
  12. 2025Developing cholera outbreak forecasting through qualitative dynamics: Insights into Malawi case studycited by 0
4 more
  1. 2025Salt-Rock Creep Deformation Forecasting Using Deep Neural Networks and Analytical Models for Subsurface Energy Storage Applicationscited by 0
  2. 2025Neural ARFIMA model for forecasting BRIC exchange rates with long memorycited by 0
  3. 2026Turning Time Series into Algebraic Equations: Symbolic Machine Learning for Interpretable Modeling of Chaotic Time Seriescited by 0
  4. 2026Long-Memory Reservoir Computing for Data-Scarce Dengue Forecastingcited by 0