Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China

Zhang, Rui; Song, Hejia; Wang, Yu; Wang, Songwang; Li, Yonghong

doi:10.1371/journal.pone.0262009

Cited by 28 publications

(14 citation statements)

References 33 publications

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“…Both ARIMA and LSTM models showed the same trends. However, LSTM is a model that can learn the long-term dependencies, and it can remember the information that is processed in the model for a very long time [22]. In terms of computational time, the ARIMA models consume more time when using the rolling forecast method, and it is unfeasible to train new models when the orders of p, d, and q increase [34].…”

Section: Discussionmentioning

confidence: 99%

“…LSTM has been extensively utilized in time series prediction in [17][18][19][20][21]. Autoregressive integrated moving average (ARIMA) is also another forecasting model [22] that predicts the future values based on the past values. ARIMA is the best model for one-step out-of-sample forecasting and is good for the data which consist of linear and short-term dependency (weekly or hourly) [23].…”

Section: Introductionmentioning

confidence: 99%

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Impact of Tree Cover Loss on Carbon Emission: A Learning‐Based Analysis

Butt

Jamshed

Rahman

et al. 2023

Computational Intelligence and Neuroscience

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Describing the processes leading to deforestation is essential for the development and implementation of the forest policies. In this work, two different learning models were developed in order to identify the best possible model for the assessment of the deforestation causes and trends. We developed autoregressive integrated moving average (ARIMA) model and long short-term memory (LSTM) independently in order to see the trend between tree cover loss and carbon dioxide emission. This study includes the twenty-year data of Pakistan on tree cover loss and carbon emission from the Global Forest Watch (GFW) platform, a known platform to get numerical data. Minimum mean absolute error (MAE) for the prediction of tree cover loss and carbon emission obtained through ARIMA model is 0.89 and 0.95, respectively. The minimum MAE given by LSTM model is 0.33 and 0.43, respectively. There is no such kind of study conducted in order to identify the increase in carbon emission due to tree cover loss most specifically in Pakistan. The results endorsed that one of the main causes of increase in the pollution in the environment in terms of carbon emission is due to tree cover loss.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Impact of Tree Cover Loss on Carbon Emission: A Learning‐Based Analysis

Butt

Jamshed

Rahman

et al. 2023

Computational Intelligence and Neuroscience

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“…We then further constructed the multivariable LSTM model, i.e., adding meteorological factors with lagged effects to the LSTM model, and the results obtained showed that the meteorological factors could improve the performance of the LSTM model, but it was still worse than the ARIMA/ARIMAX model and Holt-Winters model. The LSTM model is a complex neural network that requires a large amount of data for training, and too few training samples can lead to overfitting [ 43 ]. In this study, we are constructing a time series forecasting model based on monthly data from 2010 to 2019, with a small sample size and strong linear dependence between series; so, the ARIMA/ARIMAX model and Holt-Winters model will perform better when they have a clear trend in the series.…”

Section: Discussionmentioning

confidence: 99%

Predicting the Number of Reported Pulmonary Tuberculosis in Guiyang, China, Based on Time Series Analysis Techniques

Yang

Mao

et al. 2022

Computational and Mathematical Methods in Medicine

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Tuberculosis (TB) is one of the world’s deadliest infectious disease killers today, and despite China’s increasing efforts to prevent and control TB, the TB epidemic is still very serious. In the context of the COVID-19 pandemic, if reliable forecasts of TB epidemic trends can be made, they can help policymakers with early warning and contribute to the prevention and control of TB. In this study, we collected monthly reports of pulmonary tuberculosis (PTB) in Guiyang, China, from January 1, 2010 to December 31, 2020, and monthly meteorological data for the same period, and used LASSO regression to screen four meteorological factors that had an influence on the monthly reports of PTB in Guiyang, including sunshine hours, relative humidity, average atmospheric pressure, and annual highest temperature, of which relative humidity (6-month lag) and average atmospheric pressure (7-month lag) have a lagging effect with the number of TB reports in Guiyang. Based on these data, we constructed ARIMA, Holt-Winters (additive and multiplicative), ARIMAX (with meteorological factors), LSTM, and multivariable LSTM (with meteorological factors). We found that the addition of meteorological factors significantly improved the performance of the time series prediction model, which, after comprehensive consideration, included the ARIMAX (1,1,1) (0,1,2)12 model with a lag of 7 months at the average atmospheric pressure, outperforms the other models in terms of both fit ( RMSE = 37.570 , MAPE = 10.164 % , MAE = 28.511 ) and forecast sensitivity ( RMSE = 20.724 , MAPE = 6.901 % , MAE = 17.306 ), so the ARIMAX (1,1,1) (0,1,2)12 model with a lag of 7 months can be used as a predictor tool for predicting the number of monthly reports of PTB in Guiyang, China.

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“…These methods have just started to be applied in the prediction analysis of deformation time series 12,13 . Having outstanding performance, Autoregressive Integrated Moving Average (ARIMA), which is a traditional stochastic approach, and Long Short Term Memory (LSTM) methods were compared in many studies, and different results were achieved 14,15,16 .…”

Section: Introductionmentioning

confidence: 99%

The comparison of ARIMA and LSTM in forecasting of long-term surface movements derived from PSINSAR

Yagmur,

Musaoglu

2023

Earth Observing Systems XXVIII

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In recent years, airports, serving as vital transportation hubs, have faced the challenge of limited available land in megacities. As a result, airport construction on reclaimed areas has become a common solution. However, over time, these areas are exposed to soil behaviors like settlement and uplift, leading to surface movements. Detecting and monitoring these movements consistently is crucial to prevent potential disasters. Interferometric Synthetic Aperture Radar (InSAR) has emerged as a powerful tool for monitoring surface movements with high temporal and spatial resolution based on satellite properties, unlike traditional point-based methods. In particular, time series InSAR methods, such as Persistent Scatterer Interferometry (PSI), have been developed to monitor surface movements over a period of time. However, in addition to observing past surface movements, forecasting future movements is also of great importance. In this context, various forecasting methods have been explored, among which Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) have gained significant popularity due to their successful performance. In a recent study, these two methods were applied to forecast surface movements at Istanbul Airport, utilizing time series data obtained from the freely available Sentinel-1 SAR images. The performance of the ARIMA and LSTM models was evaluated using well-established metrics including root mean square error (RMSE) and mean absolute error (MAE). Both ARIMA and LSTM are suitable for forecasting surface movements, but LSTM exhibited a marginally better fit to the data compared to the ARIMA model.

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Comparison of ARIMA and LSTM for prediction of hemorrhagic fever at different time scales in China

Cited by 28 publications

References 33 publications

Impact of Tree Cover Loss on Carbon Emission: A Learning‐Based Analysis

Impact of Tree Cover Loss on Carbon Emission: A Learning‐Based Analysis

Predicting the Number of Reported Pulmonary Tuberculosis in Guiyang, China, Based on Time Series Analysis Techniques

The comparison of ARIMA and LSTM in forecasting of long-term surface movements derived from PSINSAR

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