Forecasting cholera disease using SARIMA and LSTM models with discrete wavelet transform as feature selection

Abstract

Throughout history, cholera has posed a public health risk, impacting vulnerable populations living in areas with contaminated water and poor sanitation. Many studies have found a high correlation between the occurrence of cholera and environmental issues such as geographical location and climate change. Developing a cholera forecasting model might be possible if a relationship exists between the cholera epidemic and meteorological elements. Given the auto-regressive character of cholera as well as its seasonal patterns, a seasonal-auto-regressive-integrated-moving-average (SARIMA) model was utilized for time-series study from 2017 to 2022 cholera datasets obtained from the NCDC. Cholera incidence correlates positively to humidity, precipitation, minimum temperature, and maximum temperature with r = 0.1045, r = 0.0175, r = 0.0666, and r = 0.0182 respectively. Improving a SARIMA model, autoregressive integrated moving average (ARIMA), and Long short-term memory (LSTM) with the k-means clustering and discrete wavelet transform (DWT) for feature selection, the improved model is known as MODIFIED SARIMA outperforms both the LSTM, ARIMA, and SARIMA and also outperformed the improved LSTM and ARIMA with an RSS = 0.502 and an accuracy = 97% .

Nonlinear Time Series Analysis and Prediction of General Aviation Accidents Based on Multi-Timescales

“…Among them, MA, and ES [26] are usually used for short-term forecasting problems. ARIMA [27] and SARIMA [28] can be applied to a wide range of time series data. VAR [29] is usually used for multivariate time series data.…”