CoChat
Sign Up
Cited by 29Forecasting Techniques and ApplicationsTime Series Analysis and ForecastingStock Market Forecasting Methods

Developing a seasonal-adjusted machine-learning-based hybrid time‑series model to forecast heatwave warning

Open access
Qureshi, Md. Mahin Uddin, Ahmed, Amrin Binte, Dulmini, Adisha, Khan, Mohammad Mahboob Hussain, Rois, Rumana·2025·Scientific Reports
DOI 10.1038/s41598-025-93227-7PMC PMC11906601
Open this paper in CoChat — free account

Abstract

Heatwaves pose a significant threat to environmental sustainability and public health, particularly in vulnerable regions and rapidly growing cities. They cause water shortages, stress on plants, and an overall drying out of landscapes, reducing plant growth-the basis of energy production and the food chain. Accurate heatwave forecasting is crucial for early warning systems, public health interventions, and disaster preparedness strategies, reducing heat-related mortality risk through modeling and evaluation of warnings. However, anticipating heatwave warnings requires handling the daily time series data, which is a large-scale and high-frequency time series data. High-frequency time series data forecasting presents unique challenges due to its inherent complexity and characteristics. Therefore, the study proposes two algorithms to develop Machine-Learning (ML)-based hybrid models as well as seasonal adjusted ML-based hybrid models, which can handle large datasets and reveal complex seasonal patterns. The performance of these developed ML-based hybrid models and seasonal adjusted ML-based hybrid models were compared with other traditional time series, Autoregressive Integrated Moving Average (ARIMA), Exponential Smoothing State Space (ETS), and Trigonometric Box-Cox ARMA Trend Seasonal (TBATS) and ML models, Artificial Neural Network (ANN), Support Vector Regression (SVR), Prophet, Random Forest Regression (RFR), and Long Short-Term Memory (LSTM), to forecast heatwave warnings in Rajshahi, one of Bangladesh's warmest districts, based on 42-year historical daily instances. Our findings indicate that the seasonal adjusted ML-based hybrid model, by integrating the Seasonal-Trend decomposition procedure based on LOESS (STL) approach with different time series and ML models, STL-ARIMA-LSTM, outperformed all other models with MAE (0.8974), MAPE (2.9232), RMSE (1.1794), MASE (0.3814) and ACF1 (0.0026). Hence, our suggested seasonal adjusted ML-based hybrid model, ensures a more accurate forecast and helps to determine the number and days of heatwaves, enabling people to plan ahead and take necessary safety measures before they occur.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41598-025-93227-7

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Prediction accuracy and heatwave projection greatly depend on the persistence of events in terms of temporal dependencies of the time series data.

    “the prediction accuracy and heatwave projection greatly depend on the persistence of events in terms of temporal dependencies of the time series data.”

    From Comparative analysis of machine learning approaches for heatwave event prediction in India (2025) · cites this paper as [54]

    The Discussion says heatwave-warning forecasts require handling daily time-series data and that seasonal adjustment matters for forecasting. The Results compare model accuracy and lag-one error autocorrelation. Neither section establishes the claimed dependence of prediction accuracy or heatwave projections on the persistence of heatwave events. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 46

  1. [1]Smith, K. R. et al. Human health: impacts, adaptation, and co-benefits. In Climate Change 2014: Impacts, Adaptation, and Vulnerability 709–754 (Cambridge University Press, 2014).
  2. [2]Coumou, D., Robinson, A. & Rahmstorf, S. Global increase in record-breaking monthly-mean temperatures. Clim. Change 118, 771–782 (2013). · DOI
  3. [3]Rahmstorf, S. & Coumou, D. Increase of extreme events in a warming world. Proc. Natl. Acad. Sci. 108, 17905–17909 (2011). 22025683 PMC3207670 · DOI
  4. [4]Hegerl, G. C. et al. Causes of climate change over the historical record. Environ. Res. Lett. 14, 123006 (2019). · DOI
  5. [5]Schär, C. et al. The role of increasing temperature variability in European summer heatwaves. Nature 427, 332–336 (2004). 14716318 · DOI
  6. [6]Argüeso, D., Di Luca, A., Perkins-Kirkpatrick, S. E. & Evans, J. P. Seasonal mean temperature changes control future heat waves. Geophys. Res. Lett. 43, 7653–7660 (2016). · DOI
  7. [8]Meehl, G. A. & Tebaldi, C. More intense, more frequent, and longer lasting heat waves in the 21st century. Science 305, 994–997 (2004). 15310900 · DOI · Paper page
  8. [10]Cowan, T. et al. More frequent, longer, and hotter heat waves for australia in the twenty-first century. J. Clim. 27, 5851–5871 (2014). · DOI
  9. [12]Trancoso, R. et al. Heatwaves intensification in Australia: A consistent trajectory across past, present and future. Sci. Total Environ. 742, 140521 (2020). 32721721 · DOI
  10. [13]Rashid, G. M., Hossain, M. M. T., Akhter, M. A. E. & Mallik, M. A. K. A study on the heat wave conditions over Bangladesh during 1990–2019. J. Eng. Sci. 14, 59–67 (2024). · DOI
  11. [28]Karmakar, S. & Das, M. K. On the heat waves in Bangladesh, their trends and associated large scale tropospheric conditions. J. Eng. Sci. 11, 19–36 (2020). · DOI
  12. [29]Huq, S. Climate change and Bangladesh. Science 294, 1617 (2001). 11721020 · DOI
  13. [30]Kirtman, B., Power, S.B., Adedoyin, A.J., Boer, G.J., Bojariu, R., Camilloni, I., Doblas-Reyes, F., Fiore, A.M., Kimoto, M., Meehl, G., Prather, M., Sarr, A., Schar, C., Sutton, R., van Oldenborgh, G.J., Vecchi, G., & Wang, H.-J. Chapter 11—Near-term climate change: Projections and predictability. In Climate Change 2013: The Physical Science Basis. IPCC Working Group I Contribution to AR5. (eds. IPCC) (Cambridge University Press, 2013).
  14. [32]Rahman, M. M. et al. Are hotspots and frequencies of heat waves changing over time? Exploring causes of heat waves in a tropical country. PLoS One 19 (5), e0300070 (2024). 38776342 PMC11111018 · DOI
  15. [33]Raja, D. R., Hredoy, M. S. N., Islam, Md. K., Islam, K. M. A. & Adnan, M. S. G. Spatial distribution of heatwave vulnerability in a coastal city of Bangladesh. Environ. Chall. 4, 100122 (2021). · DOI
  16. [34]Park, J. & Kim, J. Defining heatwave thresholds using an inductive machine learning approach. PLoS One 13, e0206872 (2018). 30403743 PMC6221332 · DOI
  17. [36]Aboubakri, O., Khanjani, N., Jahani, Y. & Bakhtiari, B. The impact of heat waves on mortality and years of life lost in a dry region of Iran (Kerman) during 2005–2017. Int. J. Biometeorol. 63, 1139–1149 (2019). 31127424 · DOI
  18. [38]Zhu, Z. & Li, T. Extended-range forecasting of Chinese summer surface air temperature and heat waves. Clim. Dyn. 50, 2007–2021 (2018). · DOI
  19. [39]Suthar, G., Singh, S., Kaul, N., Khandelwal, S. & Singhal, R. P. Prediction of maximum air temperature for defining heat wave in Rajasthan and Karnataka states of India using machine learning approach. Remote Sens. Appl. Soc. Environ. 32, 101048 (2023).
  20. [40]Leach, N. J. et al. Heatwave attribution based on reliable operational weather forecasts. Nat. Commun. 15, 4530 (2024). 38816393 PMC11140005 · DOI
  21. [41]Lee, Y. et al. Unveiling teleconnection drivers for heatwave prediction in South Korea using explainable artificial intelligence. NPJ Clim. Atmos. Sci. 7, 1–12 (2024). · DOI
  22. [44]Sultana, S.S., Mumu, S.S., Fahim, R.R., Neha, N.N. and Mahbub, M.S. Regional heatwave prediction using deep learning models in Bangladesh. In 2024 2nd International Conference on Information and Communication Technology (ICICT). 125–129 (IEEE, 2024)
  23. [45]Wen, X., Liao, J., Niu, Q., Shen, N. & Bao, Y. Deep learning-driven hybrid model for short-term load forecasting and smart grid information management. Sci. Rep. 14, 13720 (2024). 38877081 PMC11178870 · DOI
  24. [47]Garg, R., Barpanda, S., S, G. R. S. N. & S, R. Machine learning algorithms for time series analysis and forecasting. arXiv.org https://arxiv.org/abs/2211.14387 (2022). · arXiv
  25. [48]Kotu, V. & Deshpande, B. Time series forecasting. In Data Science 395–445. 10.1016/b978-0-12-814761-0.00012-5 (2019). · DOI
  26. [49]Siami-Namini, S., Tavakoli, N. & Namin, A. S. A comparison of ARIMA and LSTM in forecasting time series. In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) 1394–1401. 10.1109/icmla.2018.00227 (2018). · DOI · Paper page
  27. [50]Ho, S. L. & Xie, M. The use of ARIMA models for reliability forecasting and analysis. Comput. Ind. Eng. 35, 213–216 (1998). · DOI
  28. [51]Jofipasi, C. A., Miftahuddin, & Hizir. Selection for the best ETS (error, trend, seasonal) model to forecast weather in the Aceh Besar District. IOP Conf. Ser. Mater. Sci. Eng.352, 012055 (2018).
  29. [53]Naher, S. et al. Forecasting the incidence of dengue in Bangladesh—Application of time series model. Health Sci. Rep. 5, e666 (2022). 35702512 PMC9178403 · DOI
  30. [54]Zhao, D. & Zhang, H. The research on TBATS and ELM models for prediction of human brucellosis cases in mainland China: a time series study. BMC Infect. Dis. 22, 934 (2022). 36510150 PMC9746081 · DOI
  31. [55]Xiao, Y. et al. Estimating the long-term epidemiological trends and seasonality of hemorrhagic fever with renal syndrome in China. Infect. Drug Resist. 14, 3849–3862 (2021). 34584428 PMC8464322 · DOI
  32. [56]Yu, C. et al. Time series analysis and forecasting of the hand-foot-mouth disease morbidity in China using an advanced exponential smoothing state space TBATS model. Infect. Drug Resist. 14, 2809–2821 (2021). 34321897 PMC8312251 · DOI
  33. [57]De Livera, A. M., Hyndman, R. J. & Snyder, R. D. Forecasting time series with complex seasonal patterns using exponential smoothing. J. Am. Stat. Assoc. 106, 1513–1527 (2011). · DOI
  34. [58]Khandelwal, I., Adhikari, R. & Verma, G. Time series forecasting using hybrid ARIMA and ANN models based on DWT decomposition. Proc. Comput. Sci. 48, 173–179 (2015). · DOI
  35. [59]Zhang, G., Eddy Patuwo, B. & Hu, Y. M. Forecasting with artificial neural networks: The state of the art. Int. J. Forecast. 14, 35–62 (1998). · DOI · Paper page
  36. [60]Hamzaçebi, C. Improving artificial neural networks’ performance in seasonal time series forecasting. Inf. Sci. 178, 4550–4559 (2008). · DOI
  37. [61]Zhang, G. P. Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003). · DOI
  38. [62]Panigrahi, S. & Behera, H. S. A hybrid ETS–ANN model for time series forecasting. Eng. Appl. Artif. Intell. 66, 49–59 (2017). · DOI
  39. [63]Comito, C. & Pizzuti, C. Artificial intelligence for forecasting and diagnosing COVID-19 pandemic: A focused review. Artif. Intell. Med. 128, 102286 (2022). 35534142 PMC8958821 · DOI
  40. [65]Aditya Satrio, C. B., Darmawan, W., Nadia, B. U. & Hanafiah, N. Time series analysis and forecasting of coronavirus disease in Indonesia using ARIMA model and PROPHET. Proc. Comput. Sci.179, 524–532 (2021).
  41. [68]Elneel, L., Zitouni, M. S., Mukhtar, H. & Al-Ahmad, H. Examining sea levels forecasting using autoregressive and prophet models. Sci. Rep. 14, 14337 (2024). 38906913 PMC11192949 · DOI
  42. [71]Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. J. STL: A seasonal-trend decomposition procedure based on loess. J. Off. Stat.6(1), 3–33 (1990).
  43. [72]Kolassa, S., Rostami-Tabar, B. & Siemsen, E. Demand Forecasting for Executives and Professionals (CRC Press, 2023).
  44. [74]Bandara, K., Hyndman, R. J. & Bergmeir, C. MSTL: A Seasonal-Trend Decomposition Algorithm for Time Series with Multiple Seasonal Patterns. arXiv (Cornell University) (2021)
  45. [78]Szagri, D., Nagy, B. & Szalay, Z. How can we predict where heatwaves will have an impact? A literature review on heat vulnerability indexes. Urban Clim. 52, 101711 (2023). · DOI
  46. [81]Domeisen, D. I. et al. Prediction and projection of heatwaves. Nat. Rev. Earth Environ. 4 (1), 36–50 (2023). · DOI

Cited by

Sorted by citations

Authors
Qureshi, M. M. U., Ahmed, A. B., Dulmini, A., Khan, M. M. H., & Rois, R.
Affiliations
  • Jahangirnagar University (BD)
  • University of Wollongong (AU)
Corresponding author
  • Qureshi, Md. Mahin Uddin
  • Ahmed, Amrin Binte
  • Rois, Rumana
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2025-03-13
Volume
15
Issue
1
Pages
8699-8699
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-025-93227-7
PMC
PMC11906601
OpenAlex
W4408395707
Cited by
29
References
4
Citations by year
2026: 22 · 2025: 7
FWCI
22.30
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
  • Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
Keywords
heatwave prediction, seasonal adjustment, STL decomposition, hybrid time series models, high-frequency time series, daily temperature, heat-related mortality, Rajshahi, early warning systems
MeSH terms
Machine Learning, Algorithms, Forecasting, Hot Temperature, Humans, Models, Theoretical, Seasons, Extreme Heat

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review