ArticleNeural computing & applications2022
A novel approach based on combining deep learning models with statistical methods for COVID-19 time series forecasting.
Article in Neural computing & applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Global or local modeling for XGBoost in geospatial studies upon simulated data and German COVID-19 infection forecasting.Scientific reports · 2025Article
- Time series forecasting of Valley fever infection in Maricopa County, AZ using LSTM.Lancet regional health. Americas · 2025Article
- Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic.Life (Basel, Switzerland) · 2024Review
- Developing a Novel Methodology by Integrating Deep Learning and HMM for Segmentation of Retinal Blood Vessels in Fundus Images.Interdisciplinary sciences, computational life sciences · 2023Article
- Short-Term Forecasting of Monkeypox Cases Using a Novel Filtering and Combining Technique.Diagnostics (Basel, Switzerland) · 2023Article
- COVID-19 focused series: diagnosis and forecast of COVID-19.Journal of thoracic disease · 2023Article
- Biomarkers: Promising and valuable tools towards diagnosis, prognosis and treatment of Covid-19 and other diseases.Heliyon · 2023Review
- TSF-transformer: a time series forecasting model for exhaust gas emission using transformer.Applied intelligence (Dordrecht, Netherlands) · 2022Article
- COVID-19 Spatio-Temporal Evolution Using Deep Learning at a European Level.Sensors (Basel, Switzerland) · 2022Article
- Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics.Network modeling and analysis in health informatics and bioinformatics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The COVID-19 pandemic has disrupted the economy and businesses and impacted all facets of people's lives. It is critical to forecast the number of infected cases to make accurate decisions on the necessary measures to control the outbreak. While deep learning models have proved to be effective in this context, time series augmentation can improve their performance. In this paper, we use time series augmentation techniques to create new time series that take into account the characteristics of the original series, which we then use to generate enough samples to fit deep learning models properly. The proposed method is applied in the context of COVID-19 time series forecasting using three deep learning techniques, (1) the long short-term memory, (2) gated recurrent units, and (3) convolutional neural network. In terms of symmetric mean absolute percentage error and root mean square error measures, the proposed method significantly improves the performance of long short-term memory and convolutional neural networks. Also, the improvement is average for the gated recurrent units. Finally, we present a summary of the top augmentation model as well as a visual representation of the actual and forecasted data for each country.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.