Article
Engineering, Chemical
Mahmod Othman, Rachmah Indawati, Ahmad Abubakar Suleiman, Mochammad Bagus Qomaruddin, Rajalingam Sokkalingam
Summary: This study used time-series methods to forecast the incidence of Dengue Hemorrhagic Fever (DHF) in Surabaya City. By comparing different models, the seasonal ARIMA (SARIMA) model was found to be the most accurate forecasting method, and future outbreaks were predicted. The results showed significant seasonal outbreaks of DHF, with a significant correlation between DHF and air temperature.
Article
Public, Environmental & Occupational Health
Mostafa Majidnia, Zahra Ahmadabadi, Poneh Zolfaghari, Ahmad Khosravi
Summary: This study aimed to determine the time trend of cutaneous leishmaniasis (CL) incidence in Shahroud County, Iran using the ARIMA model. The findings suggest that time series models, particularly the SARIMA model, can be useful in predicting the incidence trends of CL and planning public health programs to reduce the cases of the disease in the coming years.
Article
Green & Sustainable Science & Technology
Mohamed Elhag, Ioannis Gitas, Anas Othman, Jarbou Bahrawi, Aris Psilovikos, Nassir Al-Amri
Summary: Monitoring water quality parameters of inland water resources in arid environments is crucial for their control and management. Results suggest that the S-ARIMA model is more reliable than the ARIMA model for predicting water quality parameters, especially in scenarios with seasonal features.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2021)
Article
Green & Sustainable Science & Technology
Ashutosh Kumar Dubey, Abhishek Kumar, Vicente Garcia-Diaz, Arpit Kumar Sharma, Kishan Kanhaiya
Summary: Energy consumption forecasting using ARIMA, SARIMA, and LSTM models based on smart meter measurements showed that humidity has a high positive correlation with energy consumption, while temperature has a high negative correlation. LSTM outperformed ARIMA and SARIMA with a mean absolute error (MAE) of 0.23.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS
(2021)
Article
Computer Science, Artificial Intelligence
Sourav Kumar Purohit, Sibarama Panigrahi, Prabira Kumar Sethy, Santi Kumari Behera
Summary: Accurate prediction of crop prices is crucial for farmers and government. This study proposes hybrid methods to predict the prices of three commonly used vegetable crops in India, showing superiority over statistical models and machine learning models through extensive statistical analyses.
APPLIED ARTIFICIAL INTELLIGENCE
(2021)
Article
Infectious Diseases
Mengmeng Zhai, Wenhan Li, Ping Tie, Xuchun Wang, Tao Xie, Hao Ren, Zhuang Zhang, Weimei Song, Dichen Quan, Meichen Li, Limin Chen, Lixia Qiu
Summary: The incidence of human brucellosis in Shanxi Province from 2007 to 2017 showed obvious seasonal characteristics. The fitting and prediction performances of the ARIMA-ERNN model were better than those of the ARIMA-BPNN and ARIMA models, providing theoretical support for infectious disease prediction and public health decision making.
BMC INFECTIOUS DISEASES
(2021)
Article
Environmental Sciences
Yanling Zheng, Kai Wang, Liping Zhang, Lei Wang
Summary: This study analyzed the impact of climate indicators on influenza incidence in Guangxi and revealed a significant association between climate factors and influenza incidence. It highlights the importance of taking precautions during severe air pollution and cold weather to prevent influenza. Furthermore, the ARIMAX model demonstrated good predictive performance, which can be utilized for predicting influenza incidence in Guangxi and informing the development of early warning systems and public health interventions for influenza control policy-making.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2021)
Article
Mathematics
Oksana Mandrikova, Nadezhda Fetisova, Yuriy Polozov
Summary: The hybrid model for time series of complex structure (HMTS) combines wavelet series with ARIMA models to provide a more accurate modeling for time series with complicated structures. The identification of HMTS anomalous components using threshold functions and the detection of ionospheric anomalies have shown the efficiency of HMTS. Comparing HMTS with the NARX neural network confirms the effectiveness of HMTS.
Article
Chemistry, Multidisciplinary
Ge Meng, Jian Liu, Rui Feng
Summary: This study accurately predicts construction accidents in China by using the trend decomposition method, autoregressive integrated moving average model, and Grey model with fractional order accumulation. The results show that the FOAGM model based on a genetic algorithm improves the prediction of accident numbers and provides hierarchical optimization for annual forecasts through rolling forecast.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Multidisciplinary
Mouna Merdasse, Mohamed Hamdache, Jose A. Pelaez, Jesus Henares, Tarek Medkour
Summary: This study compares two different time series forecasting methods (parametric and non-parametric) to predict the frequency and magnitude of earthquakes above Mw 4.0 in Northeastern Algeria. The parametric approach is based on the Autoregressive Integrated Moving Average (ARIMA) model, while the non-parametric approach utilizes the Singular Spectrum Analysis (SSA) method. The ARIMA and SSA models are trained and used to forecast the annual number of earthquakes and annual maximum magnitude events in Northeastern Algeria between 1910 and 2019. The results suggest that the SSA forecasting model is more accurate than the ARIMA model, as evaluated by the root mean square error (RMSE) criterion. According to the findings, the annual maximum magnitude in Northeastern Algeria between 2020 and 2030 is projected to range from Mw 4.8 to Mw 5.1, with an estimated four to six events of at least Mw 4.0 occurring each year.
APPLIED SCIENCES-BASEL
(2023)
Article
Engineering, Aerospace
M. Mary Victoria Florence, E. Priyadarshini
Summary: This study proposes the use of time series ARIMA models to predict the gas path performance in aero engines. The models accurately predict the performance parameters, which can improve the safety and efficiency of the engines. The methodology can be used for real-time monitoring and controlling of the gas path performance.
AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Igor Ilic, Berk Gorgulu, Mucahit Cevik, Mustafa Gokce Baydogan
Summary: Time series forecasting involves developing a model based on past observations to predict future events. The explainable boosted linear regression (EBLR) algorithm enhances predictions by explaining errors and incorporating nonlinear features. It provides interpretable results and high predictive accuracy, making it a promising method for time series forecasting.
PATTERN RECOGNITION
(2021)
Article
Mathematics, Applied
M. Dolores Ruiz-Medina
Summary: Long Range Dependence (LRD) in functional sequences is characterized in the spectral domain under suitable conditions. A weak-consistent parametric estimator of the long-memory operator is obtained by minimizing a divergence information functional loss. The results derived allow for inference from the discrete sampling of Gaussian solutions to fractional and multifractional pseudodifferential models.
FRACTIONAL CALCULUS AND APPLIED ANALYSIS
(2022)
Article
Biochemical Research Methods
Fuad Ahmed Chyon, Md Nazmul Hasan Suman, Md Rafiul Islam Fahim, Md Sazol Ahmmed
Summary: The global spread of Coronavirus Disease 2019 (COVID-19) has had a significant impact on 192 countries, with varying levels of success in containment measures. Using the Autoregressive Integrated Moving Average (ARIMA) model, this study predicts that the cumulative number of COVID-19 affected patients worldwide over the next three months will range from 9,189,262 to 14,906,483.
JOURNAL OF VIROLOGICAL METHODS
(2022)
Article
Engineering, Environmental
Vaghawan Prasad Ojha, Shantia Yarahmadian, Richard Hunt Bobo
Summary: This paper studies the short and long-run behavior of COVID-19 and analyzes the long-term behavior of the pandemic in different countries using a random evolution model. The simplicity of the model makes it a practical tool for decision-making based on the long-run behavior of the pandemic. The study shows that different phases of the pandemic and measures such as vaccination have an impact on decision-making.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2023)
