Forecasting air quality index: A statistical machine learning and deep learning approach

Background: Brucellosis runs rampant endemically with sporadic outbreaks in Algeria. The present study aimed to provide insights into the epidemiology of brucellosis and compare the performance of some prediction models using surveillance data from Tebessa province, Algeria. Study Design: A retrospective study.Methods: Seasonal autoregressive integrated moving average (SARIMA), neural network autoregressive (NNAR), and hybrid SARIMA-NNAR models were developed to predict monthly brucellosis notifications. The prediction performance of these models was compared using root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). Results: Overall, 13670 human brucellosis cases were notified in Tebessa province from 2000-2020 with a male-to-female ratio of 1.3. The most affected age group was 15-44 years (56.2%). The cases were reported throughout the year with manifest seasonality. The annual notification rate ranged from 30.9 (2013) to 246.7 (2005) per 100000 inhabitants. The disease was not evenly distributed, rather spatial and temporal variability was observed. The SARIMA (2,1,3) (1,1,1)12', NNAR (12,1,6)12' , and SARIMA (2,0,2) (1,1,0)12- NNAR (5,1,4)12 were selected as the best-fitting models. The RMSE, MAE, and MAPE of the SARIMA and SARIMA-NNAR models were by far lower than those of the NNAR model. Moreover, the SARIMA-NNNAR hybrid model achieved a slightly better prediction accuracy for 2020 than the SARIMA model. Conclusion: As evidenced by the obtained results, both SARIMA and hybrid SARIMA-NNAR models are suitable to predict human brucellosis cases with high accuracy. Reasonable predictions, along with mapping brucellosis incidence, could be of great help to veterinary and health policymakers in the development of informed, effective, and targeted policies, as well as timely interventions.

During the last decades, air pollution has become a serious environmental hazard. Its impact on public health and safety, as well as on the ecosystem, has been dramatic. Forecasting the levels of air pollution to maintain the climatic conditions and environmental protection becomes crucial for government authorities to develop strategies for the prevention of pollution. This study aims to evaluate the atmospheric air pollution of the city of Zahleh located in the geographic zone of Bekaa. The study aims to determine a relationship between variations in ambient particulate concentrations during a short time. The data was collected from June 2017 to June 2018. In order to predict the Air Quality Index (AQI), Naïve, Exponential Smoothing, TBATS (a forecasting method to model time series data), and Seasonal Autoregressive Integrated Moving Average (SARIMA) models were implemented. The performance of these models for predicting air quality is measured using the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE), and the Relative Error (RE). SARIMA model is the most accurate in prediction of AQI (RMSE = 38.04, MAE = 22.52 and RE = 0.16). The results reveal that SARIMA can be applied to cities like Zahleh to assess the level of air pollution and to prevent harmful impacts on health. Furthermore, the authorities responsible for controlling the air quality may use this model to measure the level of air pollution in the nearest future and establish a mechanism to identify the high peaks of air pollution.

BackgroundGonorrhea has long been a serious public health problem in mainland China that requires attention, modeling to describe and predict its prevalence patterns can help the government to develop more scientific interventions.MethodsTime series (TS) data of the gonorrhea incidence in China from January 2004 to August 2022 were collected, with the incidence data from September 2021 to August 2022 as the validation. The seasonal autoregressive integrated moving average (SARIMA) model, long short-term memory network (LSTM) model, and hybrid SARIMA-LSTM model were used to simulate the data respectively, the model performance were evaluated by calculating the mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) of the training and validation sets of the models.ResultsThe Seasonal components after data decomposition showed an approximate bimodal distribution with a period of 12 months. The three models identified were SARIMA(1,1,1) (2,1,2)12, LSTM with 150 hidden units, and SARIMA-LSTM with 150 hidden units, the SARIMA-LSTM model fitted best in the training and validation sets, for the smallest MAPE, RMSE, and MPE.ConclusionsThe overall incidence trend of gonorrhea in mainland China has been on the decline since 2004, with some periods exhibiting an upward trend. The incidence of gonorrhea displays a seasonal distribution, typically peaking in July and December each year. The SARIMA model, LSTM model, and SARIMA-LSTM model can all fit the monthly incidence time series data of gonorrhea in mainland China. However, in terms of predictive performance, the SARIMA-LSTM model outperforms the SARIMA and LSTM models, with the LSTM model surpassing the SARIMA model. This suggests that the SARIMA-LSTM model can serve as a preferred tool for time series analysis, providing evidence for the government to predict trends in gonorrhea incidence. The model's predictions indicate that the incidence of gonorrhea in mainland China will remain at a high level in 2024, necessitating that policymakers implement public health measures in advance to prevent the spread of the disease.

This paper compares four practical methods for electricity generation forecasting of grid-connected Photovoltaic (PV) plants, namely Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling, SARIMAX modeling (SARIMA modeling with exogenous factor), modified SARIMA modeling, as a result of an a posteriori modification of the SARIMA model, and ANN-based modeling. Interesting results regarding the necessity and the advantages of using exogenous factors in a time series model are concluded from this comparison. Finally, intra-day forecasts updates are implemented to evaluate the forecasting errors of the SARIMA and the SARIMAX models. Their comparison highlights differences in accuracy between the two models. All models are compared in terms of the Normalized (with respect to the PV installed capacity) Root Mean Square Error (NRMSE) criterion. Simulation results from the application of the forecasting models in a PV plant in Greece using real-world data are presented.

BackgroundRed blood cells (RBCs) infusion is very important for the treatment of hematology patients, but how to maintain a balanced state between the supply and demand of RBCs is still a major challenge.ObjectiveThis study aimed to explore the feasibility of seasonal autoregressive integrated moving average (SARIMA) model and exponential smoothing (ES) model in predicting the clinical demand of RBCs for hematology patients each month.MethodsOur study collected the monthly RBCs usage data of hematology patients from January 2014 to December 2023 to establish the SARIMA model and ES model, respectively. Then, the optimal model was used to forecast the monthly usage of RBCs from January to June 2024, and we subsequently compared the data with actual values to evaluate the prediction effect of the model.ResultsThe best fitting SARIMA model was SARIMA (2,1,0)(1,1,1)12, whose R2 = 0.603, MAE = 37.092, MAPE = 13.693, BIC = 7.896. The best fitting ES model was Winters addition model, whose R2 = 0.702, MAE = 32.617, MAPE = 12.138, BIC = 7.485. The mean relative errors of two models were 0.085 and 0.159, respectively. The SARIMA (2,1,0)(1,1,1)12 model performed better in prediction.ConclusionCompared with the ES model, the SARIMA model has a smaller mean relative error in predicting RBCs usage in hematology patients. DM test also verify this result. But in the future, more similar research data are needed to make research more convincing.

The primary objective of this study is to evaluate the accuracy of different forecasting models for monthly wind farm electricity production. This study compares the effectiveness of three forecasting models: Autoregressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), and Support Vector Regression (SVR). This study utilizes data from two wind farms located in Poland—‘Gizałki’ and ‘Łęki Dukielskie’—to exclude the possibility of biased results due to specific characteristics of a single farm and to allow for a more comprehensive comparison of the effectiveness of both time series analysis methods. Model parameterization was optimized through a grid search based on the Mean Absolute Percentage Error (MAPE). The performance of the best models was evaluated using Mean Bias Error (MBE), MAPE, Mean Absolute Error (MAE), and R2Score. For the Gizałki farm, the ARIMA model outperformed SARIMA and SVR, while for the Łęki Dukielskie farm, SARIMA proved to be the most accurate, highlighting the importance of optimizing seasonal parameters. The SVR method demonstrated the lowest effectiveness for both datasets. The results indicate that the ARIMA and SARIMA models are effective for forecasting wind farm energy production. However, their performance is influenced by the specificity of the data and seasonal patterns. The study provides an in-depth analysis of the results and offers suggestions for future research, such as extending the data to include multidimensional time series. Our findings have practical implications for enhancing the accuracy of wind farm energy forecasts, which can significantly improve operational efficiency and planning.

This study evaluates the effectiveness of Seasonal Autoregressive Integrated Moving Average (SARIMA) models in forecasting trends of Pediatric Diarrhea and Pneumonia in Rivers State, Nigeria, with particular emphasis on handling auto-correlated errors. The objectives include analyzing seasonal patterns, assessing autocorrelation structures, comparing alternative SARIMA specifications, and identifying the most suitable model using both real and simulated datasets. The empirical data comprise monthly records of reported cases among children under five years, obtained from the Rivers State Primary Healthcare Database. Additionally, a synthetic time series was generated to replicate similar autocorrelation characteristics for robustness testing. Autocorrelation diagnostics, including the Ljung-Box test and ACF/PACF plots, revealed significant serial dependencies across all series, underscoring the limitations of models that ignore autocorrelation. Competing SARIMA models were estimated and evaluated based on Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Root Mean Square Error (RMSE), and residual diagnostic checks. Among the models assessed, SARIMA(0,1,2)(0,0,2)[12][12][12] emerged as the best-fitting model, particularly for pediatric diarrhea, demonstrating strong predictive performance and adherence to diagnostic assumptions. Although Auto ARIMA achieved comparable results for pneumonia, SARIMA models exhibited superior residual behaviour and diagnostic reliability. By integrating actual and simulated data, this research addresses gaps in previous studies that overlooked seasonality and autocorrelation in padiatric health time series modeling. The findings affirm the usefulness of SARIMA for time series epidemiology and highlight its potential for enhancing disease investigation and informing public health interventions in resource-constrained settings such as Rivers State.

Dengue hemorrhagic fever (DHF) is one of the most widespread and deadly diseases in several parts of Indonesia. An accurate forecast-based model is required to reduce the incidence rate of this disease. Time-series methods such as autoregressive integrated moving average (ARIMA) models are used in epidemiology as statistical tools to study and forecast DHF and other infectious diseases. The present study attempted to forecast the monthly confirmed DHF cases via a time-series approach. The ARIMA, seasonal ARIMA (SARIMA), and long short-term memory (LSTM) models were compared to select the most accurate forecasting method for the deadly disease. The data were obtained from the Surabaya Health Office covering January 2014 to December 2016. The data were partitioned into the training and testing sets. The best forecasting model was selected based on the lowest values of accuracy metrics such as the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The findings demonstrated that the SARIMA (2,1,1) (1,0,0) model was able to forecast the DHF outbreaks in Surabaya City compared to the ARIMA (2,1,1) and LSTM models. We further forecasted the DHF cases for 12 month horizons starting from January 2017 to December 2017 using the SARIMA (2,1,1) (1,0,0), ARIMA (2,1,1), and LSTM models. The results revealed that the SARIMA (2,1,1) (1,0,0) model outperformed the ARIMA (2,1,1) and LSTM models based on the goodness-of-fit measure. The results showed significant seasonal outbreaks of DHF, particularly from March to September. The highest cases observed in May suggested a significant seasonal correlation between DHF and air temperature. This research is the first attempt to analyze the time-series model for DHF cases in Surabaya City and forecast future outbreaks. The findings could help policymakers and public health specialists develop efficient public health strategies to detect and control the disease, especially in the early phases of outbreaks.

Dengue fever has been a continued public health problem in Bangladesh, with a recent surge in cases. The aim of this study was to train ARIMA and SARIMA models for time series analysis on the monthly prevalence of dengue in Bangladesh and to use these models to forecast the dengue prevalence for the next 12 months. This secondary data-based study utilizes AutoRegressive Integrated Moving Average (ARIMA) and Seasonal AutoRegressive Integrated Moving Average (SARIMA) models to forecast dengue prevalence in Bangladesh. Data was sourced from the Institute of Epidemiology Disease Control and Research (IEDCR) and the Directorate General of Health Services (DGHS). STROBE Guideline for observational studies was followed for reporting this study. The ARIMA (1,1,1) and SARIMA (1,1,2) models were identified as the best-performing models. The forecasts indicate a steady dengue prevalence for 2024 according to ARIMA, while SARIMA predicts significant fluctuations. It was observed that ARIMA (1,1,1) and SARIMA (1,2,2) (1,1,2) 12 were the most suitable models for prediction of dengue prevalence. These models offer valuable insights for healthcare planning and resource allocation, although external factors and complex interactions must be considered. Dengue prevalence is expected to rise in future in Bangladesh.

Hydraulic support plays a key role in ground control of longwall mining. The smart prediction methods of support load are important for achieving intelligent mining. In this paper, the hydraulic support load data is decomposed into trend term, cycle term, and residual term, and it is found that the data has clear trend and period features, which can be called time series data. Based on the autoregression theory and weighted moving average method, the time series model is built to analyze the load data and predict its evolution trend, and the prediction accuracy of the sliding window model, ARIMA (Autoregressive Integrated Moving Average) model, and SARIMA (Seasonal Autoregressive Integrated Moving Average) model to the hydraulic support load under different parameters are evaluated, respectively. The results of single-point and multipoint prediction test with various sliding window values indicate that the sliding window method has no advantage in predicting the trend of the support load. The ARIMA model shows a better short-term trend prediction than the sliding window model. To some extent, increasing the length of the autoregressive term can improve the long-term prediction accuracy of the model, but it also increases the sensitivity of the model to support load fluctuation, and it is still difficult to predict the load trend in one support cycle. The SARIMA model has better prediction results than the sliding window model and the ARIMA model, which reveals the load evolution trend accurately during the whole support cycle. However, there are many external factors affecting the support load, such as overburden properties, hydraulic support moving speed, and worker’s operation. The smarter model of SARIMA considering these factors should be developed to be more suitable in predicting the hydraulic support load.

BackgroundThe early warning model of infectious diseases plays a key role in prevention and control. This study aims to using seasonal autoregressive fractionally integrated moving average (SARFIMA) model to predict the incidence of hemorrhagic fever with renal syndrome (HFRS) and comparing with seasonal autoregressive integrated moving average (SARIMA) model to evaluate its prediction effect.MethodsData on notified HFRS cases in Weifang city, Shandong Province were collected from the official website and Shandong Center for Disease Control and Prevention between January 1, 2005 and December 31, 2018. The SARFIMA model considering both the short memory and long memory was performed to fit and predict the HFRS series. Besides, we compared accuracy of fit and prediction between SARFIMA and SARIMA which was used widely in infectious diseases.ResultsModel assessments indicated that the SARFIMA model has better goodness of fit (SARFIMA (1, 0.11, 2)(1, 0, 1)12: Akaike information criterion (AIC):-631.31; SARIMA (1, 0, 2)(1, 1, 1)12: AIC: − 227.32) and better predictive ability than the SARIMA model (SARFIMA: root mean square error (RMSE):0.058; SARIMA: RMSE: 0.090).ConclusionsThe SARFIMA model produces superior forecast performance than the SARIMA model for HFRS. Hence, the SARFIMA model may help to improve the forecast of monthly HFRS incidence based on a long-range dataset.

Problem statement: Most of Seasonal Autoregressive Integrated Moving Average (SARIMA) models that used for forecasting seasonal time series are multiplicative SARIMA models. These models assume that there is a significant parameter as a result of multiplication between nonseasonal and seasonal parameters without testing by certain statistical test. Moreover, most popular statistical software such as MINITAB and SPSS only has facility to fit a multiplicative model. The aim of this research is to propose a new procedure for indentifying the most appropriate order of SARIMA model whether it involves subset, multiplicative or additive order. In particular, the study examined whether a multiplicative parameter existed in the SARIMA model. Approach: Theoretical derivation about Autocorrelation (ACF) and Partial Autocorrelation (PACF) functions from subset, multiplicative and additive SARIMA model was firstly discussed and then R program was used to create the graphics of these theoretical ACF and PACF. Then, two monthly datasets were used as case studies, i.e. the international airline passenger data and series about the number of tourist arrivals to Bali, Indonesia. The model identification step to determine the order of ARIMA model was done by using MINITAB program and the model estimation step used SAS program to test whether the model consisted of subset, multiplicative or additive order. Results: The theoretical ACF and PACF showed that subset, multiplicative and additive SARIMA models have different patterns, especially at the lag as a result of multiplication between non-seasonal and seasonal lags. Modeling of the airline data yielded a subset SARIMA model as the best model, whereas an additive SARIMA model is the best model for forecasting the number of tourist arrivals to Bali. Conclusion: Both of case studies showed that a multiplicative SARIMA model was not the best model for forecasting these data. The comparison evaluation showed that subset and additive SARIMA models gave more accurate forecasted values at out-sample datasets than multiplicative SARIMA model for airline and tourist arrivals datasets respectively. This study is valuable contribution to the Box-Jenkins procedure particularly at the model identification and estimation steps in SARIMA model. Further work involving multiple seasonal ARIMA models, such as short term load data forecasting in certain countries, may provide further insights regarding the subset, multiplicative or additive orders.

In this research, we propose an extreme values measure, the Value-at-Risk (VaR) based Seasonal Trend Loess (STL) Decomposition and Seasonal Autoregressive Integrated Moving Average (SARIMA) models, which is more sensitive to the seasonality of extreme value than the conventional VaR. We consider the problem of the seasonality and extreme value for increment rate of Covid-19 forecasting. For stakeholder, government and regulator, VaR estimation can be implemented to face the extreme wave of new positive Covid-19 in the future and minimize the losses that possibly affected in term of financial and human resources. Specifically, the estimation of VaR is developed with the difference lies on parameter estimators of STL and SARIMA model. The VaR has coverage probability as well as close 1-α. Thus, we propose to set α as parameter to estimate VaR. Consequently, the performance of VaR will depend not only on parameter model but also α. Our aim estimates VaR with minimum α based on correct VaR value. Numerical analysis is carried out to illustrate the estimative VaR.

Air quality index forecast in Beijing based on CNN-LSTM multi-model

This study investigates the forecasting accuracy of three univariate time series models—Decomposition, Winter’s method, and Seasonal Autoregressive Integrated Moving Average (SARIMA) to predict the Agricultural GDP of Nigeria. Quarterly data on Nigeria's Agricultural GDP from 2010 to the first quarter of 2023, obtained from the National Bureau of Statistics, were analyzed. The study applied Box-Jenkins SARIMA modeling, time series decomposition, and Winter’s method to compare their forecasting accuracy using Root Mean Square Error (RMSE) as the selection criterion. The results revealed that the SARIMA (0, 0, 2)(2, 1, 0) model outperformed the other methods, with the lowest RMSE, indicating its superior accuracy in forecasting Agricultural GDP. Winter’s method and the Decomposition method. The forecast from the SARIMA model indicated a positive trend in Nigeria’s Agricultural GDP over the study period, reinforcing the sector’s critical role in economic growth.