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

arXiv: https://arxiv.org/abs/2206.10696 · HTML: https://mlsanity.com/paper/2206.10696/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 199 references that are in the map.
- First posted on arXiv 21 Jun 2022.

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

- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks](https://mlsanity.com/paper/1704.04110/) (2017)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Monash Time Series Forecasting Archive](https://mlsanity.com/paper/2105.06643/) (2021)
- [Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case](https://mlsanity.com/paper/2001.08317/) (2020)
- [Darts: User-Friendly Modern Machine Learning for Time Series](https://mlsanity.com/paper/2110.03224/) (2021)
- [Probabilistic Forecasting with Temporal Convolutional Neural Network](https://mlsanity.com/paper/1906.04397/) (2019)
- [Forecasting: theory and practice](https://mlsanity.com/paper/2012.03854/) (2020)
- [Phenomenological forecasting of disease incidence using heteroskedastic Gaussian processes: a dengue case study](https://mlsanity.com/paper/1702.00261/) (2017)
- [A Nonparametric Ensemble Binary Classifier and its Statistical Properties](https://mlsanity.com/paper/1804.10928/) (2018)

## Built on by (most cited first, 16 of 16)

- [Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art](https://mlsanity.com/paper/2308.16316/) (2023)
- [W-Transformers : A Wavelet-based Transformer Framework for Univariate Time Series Forecasting](https://mlsanity.com/paper/2209.03945/) (2022)
- [Randomized prior wavelet neural operator for uncertainty quantification](https://mlsanity.com/paper/2302.01051/) (2023)
- [Epidemic-guided deep learning for spatiotemporal forecasting of Tuberculosis outbreak](https://mlsanity.com/paper/2502.10786/) (2025)
- [Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemics](https://mlsanity.com/paper/2603.07108/) (2026)
- [An ensemble neural network approach to forecast Dengue outbreak based on climatic condition](https://mlsanity.com/paper/2212.08323/) (2022)
- [Spatiotemporal Forecasting of Traffic Flow using Wavelet-based Temporal Attention](https://mlsanity.com/paper/2407.04440/) (2024)
- [EpiCastBench: Datasets and Benchmarks for Multivariate Epidemic Forecasting](https://mlsanity.com/paper/2605.11598/) (2026)
- [Knowledge-based Deep Learning for Modeling Chaotic Systems](https://mlsanity.com/paper/2209.04259/) (2022)
- [Machine Learning for Infectious Disease Risk Prediction: A Survey](https://mlsanity.com/paper/2308.03037/) (2023)
- [WaveCatBoost for Probabilistic Forecasting of Regional Air Quality Data](https://mlsanity.com/paper/2404.05482/) (2024)
- [Developing cholera outbreak forecasting through qualitative dynamics: Insights into Malawi case study](https://mlsanity.com/paper/2503.14009/) (2025)
- [Salt-Rock Creep Deformation Forecasting Using Deep Neural Networks and Analytical Models for Subsurface Energy Storage Applications](https://mlsanity.com/paper/2508.05248/) (2025)
- [Neural ARFIMA model for forecasting BRIC exchange rates with long memory](https://mlsanity.com/paper/2509.06697/) (2025)
- [Turning Time Series into Algebraic Equations: Symbolic Machine Learning for Interpretable Modeling of Chaotic Time Series](https://mlsanity.com/paper/2603.07261/) (2026)
- [Long-Memory Reservoir Computing for Data-Scarce Dengue Forecasting](https://mlsanity.com/paper/2607.11272/) (2026)

## 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.