Time series analysis and forecasting of cholera disease using discrete wavelet transform and seasonal autoregressive integrated moving average model
Purpose: Cholera is among the leading causes of death in Nigeria. The main predictors of cholera transmission remain the lack of access to potable water and good sanitary conditions. Cholera is also linked to weather variables such as maximum temperatures, high Rainfall, and humidity. The relationsh...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | Scientific African |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468227623001096 |
| _version_ | 1797802179042476032 |
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| author | Ahmad Hauwa Amshi Rajesh Prasad |
| author_facet | Ahmad Hauwa Amshi Rajesh Prasad |
| author_sort | Ahmad Hauwa Amshi |
| collection | DOAJ |
| description | Purpose: Cholera is among the leading causes of death in Nigeria. The main predictors of cholera transmission remain the lack of access to potable water and good sanitary conditions. Cholera is also linked to weather variables such as maximum temperatures, high Rainfall, and humidity. The relationship between cholera cases and weather variables depends on location, time, or season; hence, it is a time series dataset. This research aims to enhance the seasonal autoregressive integrated moving average (SARIMA) model by incorporating the discrete wavelet transform (DWT). Methods: This research proposed a novel approach to forecasting cholera using the SARIMA model by incorporating DWT as a dimensionality reduction technique and a K-means clustering algorithm for outlier detection. The enhanced model is termed the ''Enhanced seasonal autoregressive integrated moving average'' (ESARIMA). DWT is a good dimensionality reduction technique for time series data and extracts the best features for forecasting to have better prediction accuracy and minimal error. Result: The results show that ESARIMA (accuracy = 97%, RSS = 0.502) outperformed the existing model, SARIMA (accuracy = 91.61%, RSS = 0.60). Conclusion: Nigeria's weekly and monthly cholera outbreaks exhibit stochastic seasonal time series behavior that becomes stationary after the first seasonal differencing; hence, it could be forecasted with specific time series models. |
| first_indexed | 2024-03-13T05:01:48Z |
| format | Article |
| id | doaj.art-7c0589bc53d7479e871cc5e014834a68 |
| institution | Directory Open Access Journal |
| issn | 2468-2276 |
| language | English |
| last_indexed | 2024-03-13T05:01:48Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Scientific African |
| spelling | doaj.art-7c0589bc53d7479e871cc5e014834a682023-06-17T05:19:50ZengElsevierScientific African2468-22762023-07-0120e01652Time series analysis and forecasting of cholera disease using discrete wavelet transform and seasonal autoregressive integrated moving average modelAhmad Hauwa Amshi0Rajesh Prasad1Department of Computer Science, African University of Science and Technology, Abuja, NigeriaCorresponding author.; Department of Computer Science, African University of Science and Technology, Abuja, NigeriaPurpose: Cholera is among the leading causes of death in Nigeria. The main predictors of cholera transmission remain the lack of access to potable water and good sanitary conditions. Cholera is also linked to weather variables such as maximum temperatures, high Rainfall, and humidity. The relationship between cholera cases and weather variables depends on location, time, or season; hence, it is a time series dataset. This research aims to enhance the seasonal autoregressive integrated moving average (SARIMA) model by incorporating the discrete wavelet transform (DWT). Methods: This research proposed a novel approach to forecasting cholera using the SARIMA model by incorporating DWT as a dimensionality reduction technique and a K-means clustering algorithm for outlier detection. The enhanced model is termed the ''Enhanced seasonal autoregressive integrated moving average'' (ESARIMA). DWT is a good dimensionality reduction technique for time series data and extracts the best features for forecasting to have better prediction accuracy and minimal error. Result: The results show that ESARIMA (accuracy = 97%, RSS = 0.502) outperformed the existing model, SARIMA (accuracy = 91.61%, RSS = 0.60). Conclusion: Nigeria's weekly and monthly cholera outbreaks exhibit stochastic seasonal time series behavior that becomes stationary after the first seasonal differencing; hence, it could be forecasted with specific time series models.http://www.sciencedirect.com/science/article/pii/S2468227623001096Cholera forecastingDiscrete wavelet transformSARIMAARIMALSTMRSS |
| spellingShingle | Ahmad Hauwa Amshi Rajesh Prasad Time series analysis and forecasting of cholera disease using discrete wavelet transform and seasonal autoregressive integrated moving average model Scientific African Cholera forecasting Discrete wavelet transform SARIMA ARIMA LSTM RSS |
| title | Time series analysis and forecasting of cholera disease using discrete wavelet transform and seasonal autoregressive integrated moving average model |
| title_full | Time series analysis and forecasting of cholera disease using discrete wavelet transform and seasonal autoregressive integrated moving average model |
| title_fullStr | Time series analysis and forecasting of cholera disease using discrete wavelet transform and seasonal autoregressive integrated moving average model |
| title_full_unstemmed | Time series analysis and forecasting of cholera disease using discrete wavelet transform and seasonal autoregressive integrated moving average model |
| title_short | Time series analysis and forecasting of cholera disease using discrete wavelet transform and seasonal autoregressive integrated moving average model |
| title_sort | time series analysis and forecasting of cholera disease using discrete wavelet transform and seasonal autoregressive integrated moving average model |
| topic | Cholera forecasting Discrete wavelet transform SARIMA ARIMA LSTM RSS |
| url | http://www.sciencedirect.com/science/article/pii/S2468227623001096 |
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