ARIMAX Model Research Articles

Construction of Prediction Model of Foodborne Disease Outbreaks and Its Trend Prediction - Guizhou Province, China, 2023-2025.

Foodborne diseases pose a significant public health concern globally. This study aims to analyze the correlation between disease prevalence and climatic conditions, forecast the pattern of foodborne disease outbreaks, and offer insights for effective prevention and control strategies and optimizing health resource allocation policies in Guizhou Province. This study utilized the χ2 test and four comprehensive prediction models to analyze foodborne disease outbreaks recorded in the Guizhou Foodborne Disease Outbreak system between 2012 and 2022. The best-performing model was chosen to forecast the trend of foodborne disease outbreaks in Guizhou Province, 2023-2025. Significant variations were observed in the incidence of foodborne disease outbreaks in Guizhou Province concerning various meteorological factors (all P≤0.05). Among all models, the SARIMA-ARIMAX combined model demonstrated the most accurate predictive performance (RMSE: Prophet model=67.645, SARIMA model=3.953, ARIMAX model=26.544, SARIMA-ARIMAX model=26.196; MAPE: Prophet model=42.357%, SARIMA model=37.740%, ARIMAX model=15.289%, SARIMA-ARIMAX model=13.961%). The analysis indicates that foodborne disease outbreaks in Guizhou Province demonstrate distinct seasonal patterns. It is recommended to concentrate prevention efforts during peak periods. The SARIMA-ARIMAX hybrid model enhances the precision of monthly forecasts for foodborne disease outbreaks, offering valuable insights for future prevention and control strategies.

ARIMA and ARIMAX Analysis on the Effect of Variability of Rainfall, Temperature on Wheat Yield in Haryana

The national and state governments require crop production forecasts to make a variety of policy decisions on import-export, storage, distribution, price, and other factors. This article presents a pre-harvest forecasting method specially developed for crops grown in the western region of Haryana (India). The western region includes Hisar, Sirsa, and Bhiwani districts. For crop forecasting in the Hisar, Sirsa, and Bhiwani regions ARIMA and ARIMAX models have been framed. For the development of the ARIMAX model, climate data during the growing season of the crop were used as input along with the crop yield. The percentage difference between the root mean square error and the wheat yield estimations determined by the real-time yield(s) indicates how well-competing models performed in terms of forecasting. The ARIMAX model performs well at all time points with a lower measurement error compared to the ARIMA model.

Emergence of SARS-CoV-2 Variants Are Induced by Coinfections With Dengue.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that emerged in late 2019 has accumulated a series of point mutations and evolved into several variants of concern (VOCs), some of which are more transmissible and potentially more severe than the original strain. The most notable VOCs are Alpha, Beta, Gamma, Delta, and Omicron, which have spread to various parts of the world. This study conducted surveillance in Jashore, Bangladesh to identify the prevalence of SARS-CoV-2 coinfected with dengue virus and their genomic effect on the emergence of VOCs. A hospital-based COVID-19 surveillance from June to August, 2021 identified 9 453 positive patients in the surveillance area. The study enrolled 572 randomly selected COVID-19-positive patients, of which 11 (2%) had dengue viral coinfection. Whole genome sequences of SARS-CoV-2 were analyzed and compared between coinfection positive and negative group. In addition, we extracted 185 genome sequences from GISAID to investigate the cross-correlation function between SARS-CoV-2 mutations and VOC; multiple ARIMAX(p,d,q) models were developed to estimate the average number of amino acid (aa) substitution among different SARS-CoV-2 VOCs. The results of the study showed that the coinfection group had an average of 30.6 (±1.7) aa substitutions in SARS-CoV-2, whereas the dengue-negative COVID-19 group had that average of 25.6 (±1.8; P < .01). The coinfection group showed a significant difference of aa substitutions in open reading frame (ORF) and N-protein when compared to dengue-negative group (P = .03). Our ARIMAX models estimated that the emergence of SARS-CoV-2 variants Delta required additional 9 to 12 aa substitutions than Alpha, Beta, or Gamma variant. The emergence of Omicron accumulated additional 19 (95% confidence interval [CI]: 15.74, 21.95) aa substitution than Delta. Increased number of point mutations in SARS-CoV-2 genome identified from coinfected cases could be due to the compromised immune function of host and induced adaptability of pathogens during coinfections. As a result, new variants might be emerged when series of coinfection events occur during concurrent two epidemics.

Leveraging Exogenous Insights: A Comparative Forecast of Paddy Production in Nepal Using ARIMA and ARIMAX Models

Using annual time series data from 1975 to 2022, this study analyzed the ARIMA (1,1,7) and ARIMAX (1,1,7) models to improve paddy production forecasts in Nepal. The ARIMA model was initially employed to forecast paddy production. The availability of agricultural land was subsequently included as an exogenous variable in the ARIMAX model (after a significant endogeneity test) to increase precision. In contrast to the ARIMA model, which predicted paddy production of 5787.64 metric tons per hectare for the year 2022, the ARIMAX model predicted 5681.17 metric tons per hectare. Compared to the ARIMA model's MAE (0.0295), MAPE (0.797), and RMSE (0.0373), the ARIMAX model exhibited better accuracy with lower values of MAE (0.0247), MAPE (0.667), and RMSE (0.0301). The results emphasize how crucial it is to include pertinent exogenous variables to capture important dynamics influencing agricultural output. The study has important ramifications for Nepalese policymakers and agricultural planners since it suggests that the ARIMAX model can provide additional insight for policies that maximize production and optimize land usage, hence promoting food security and economic stability.

COVID-19'UN İSLAMİ VE GELENEKSEL HİSSE SENEDİ ENDEKSLERİ ÜZERİNDEKİ ETKİSİ

The objectives of this research are to study Islamic stock indexes during the time of Covid-19 extensively and compare it with conventional stock indexes. Our research aims to analyse how stock returns indexes of Islamic and conventional have been affected by COVID-19. So, in other word, the research objected to analyse the effect of COVID-19 on the returns and volatility of Islamic and conventional stocks indexes by using ARIMA-X and EGARCH-X models. In keeping with this objective, the Islamic and conventional stocks indexes were used to estimate the financial stock return, and the dummy variable as of number of everyday cases of each country was used to estimate the effect of COVID-19.&#x0D; The research investigates the levels of uncertainty and volatility in Islamic and conventional stock indexes prior to and during the Covid-19 crisis. Additionally, it compares Islamic stock markets with others and analyses the impact of the pandemic on these markets. The findings reveal statistically significant results, with certain indexes demonstrating significance at levels of 10%, 5%, and 1%.&#x0D; By overall looking we can say that Nigerian indexes were affected the most among others which include the Islamic and the conventional, moreover the least affected by the uncertainty was in Indonesia and Malaysia and the Islamic indexes in general were better. We can conclude that Islamic stock indexes were affected like others by the COVID-19.

Predicting Pressure from Environmental Factors in London based on ARIMAX Model

Pressure is an important index in atmospheric science as it influences various aspects of both the natural environment and everyday life. Although the significance of pressure is widely recognized, accurately measuring and recording pressure data remains a crucial issue. This is due to the fact that weather prediction involves fitting a large number of nonlinear factors, which poses significant challenges in establishing precise predicting models. Therefore, the current focus of most research on pressure lies in the innovation and improvement of algorithms for pressure sensors and other related devices. This paper makes a statistical analysis on the raw climate data of London over a period of 13 years starting from 2008, applying an ARIMAX (Autoregressive Integrated Moving Average with Exogenous Variables) dynamic regression model to fit 4 exogenous variables to the pressure data as linear factors. The fitting results showed that the variation in pressure is associated with multiple environmental factors. Furthermore, it is observed that the pressure in London is projected to be slightly higher than normal one in the upcoming months. Due to the numerous parameters in the ARIMAX model, overfitting and underfitting phenomena are prone to occur, therefore, for parameter adjustments, further processing is required.

Shanghai Stock Exchange Composite Index Prediction Using Macro Variables Based on ARIMAX Model

Stock market prediction has always been a prevailing topic among investors and researchers. Among numerous market index, the Shanghai Stock Exchange Composite Index (SSE Index) is recognized as one of the most indicative stock indexes in China's A-share market. As it is discovered that the fluctuations of SSE index and exchange rate (USD/CNY) displays highly similar pattern since the subprime mortgage crisis, this study aims to use macro variables (including exchange rate) to predict the SSE index based on ARIMAX model. The data are collected since 2006 and based on which ARIMA (14,1,4) model is generated. Distinct macro variables are added in this ARIMA model respectively and it concludes that the incorporation of exchange rate with certain lags can significantly increase the fitting degree. Cross validations on different lengths of validation sets are implemented, which shows that the model can make accurate forecast in relatively short term. The result manifests increasing accuracy when incorporating certain explanatory regressive factors, which might provide valuable and enlightening information in short term for researchers or investors.

Performance Analysis of Auto ARIMAX Model with Facebook Prophet and Light GBM Forecasting Models: Affirmation from Indian Mutual Fund

Performance Analysis of Auto ARIMAX Model with Facebook Prophet and Light GBM Forecasting Models: Affirmation from Indian Mutual Fund

Enhancing the ARIMAX model by using the bivariate wavelet denoising: Application on road traffic accidents

Enhancing the ARIMAX model by using the bivariate wavelet denoising: Application on road traffic accidents

US Inflation Analysis and Forecast Based on US-China Trade Perspective

Since the US initiated the trade war and sought the decoupling of trade between China and the US, the US economic and financial situation has taken a sharp turn and inflation has remained high. So, this article studied the inflation analysis of the United States from the perspective of Sino-US trade and made a forecast. This paper constructs an ARIMAX model to empirically analyze the relationship between US-China trade and US inflation. The study shows that there is a significant positive correlation between China's export price index and the US CPI, which shows that China and the US are important stakeholders of each other. Because the United States is one of the largest economies, its economic development is of great significance to the world economic pattern and affects the whole world. Through the economic and trade cooperation between China and the United States, the inflation rate of the United States will be reduced, and the economic growth of the United States will be promoted.

Enhancing the ARIMAX model by using the bivariate wavelet denoising: Application on road traffic accidents

Enhancing the ARIMAX model by using the bivariate wavelet denoising: Application on road traffic accidents

Revisiting the hysteresis hypothesis: an ARIMAX approach

The 2008 financial crisis saw a resurgence of interest in the hysteresis hypothesis, which was subsequently reflected in policy responses to the COVID-19 and cost-of-living crises. In this paper, we present new evidence in favour of the hysteresis hypothesis for Germany, France, and the United Kingdom, using a dataset that spans from 1960 to 2019. Our model is based on the popular unobserved components approach to estimating the degree of hysteresis, which is generalised to permit a reduced form Phillips curve that takes the form of an ARIMAX model. Our results are robust to ARCH effects and varying the sample span. They support contemporary warnings of the risk of scarring effects following the COVID-19 crisis, long-standing Post-Keynesian models of hysteresis, and the recent resurgence of mainstream interest in hysteresis.

Early Warning and Prediction of Scarlet Fever in China Using the Baidu Search Index and Autoregressive Integrated Moving Average With Explanatory Variable (ARIMAX) Model: Time Series Analysis.

Internet-derived data and the autoregressive integrated moving average (ARIMA) and ARIMA with explanatory variable (ARIMAX) models are extensively used for infectious disease surveillance. However, the effectiveness of the Baidu search index (BSI) in predicting the incidence of scarlet fever remains uncertain. Our objective was to investigate whether a low-cost BSI monitoring system could potentially function as a valuable complement to traditional scarlet fever surveillance in China. ARIMA and ARIMAX models were developed to predict the incidence of scarlet fever in China using data from the National Health Commission of the People's Republic of China between January 2011 and August 2022. The procedures included establishing a keyword database, keyword selection and filtering through Spearman rank correlation and cross-correlation analyses, construction of the scarlet fever comprehensive search index (CSI), modeling with the training sets, predicting with the testing sets, and comparing the prediction performances. The average monthly incidence of scarlet fever was 4462.17 (SD 3011.75) cases, and annual incidence exhibited an upward trend until 2019. The keyword database contained 52 keywords, but only 6 highly relevant ones were selected for modeling. A high Spearman rank correlation was observed between the scarlet fever reported cases and the scarlet fever CSI (rs=0.881). We developed the ARIMA(4,0,0)(0,1,2)(12) model, and the ARIMA(4,0,0)(0,1,2)(12) + CSI (Lag=0) and ARIMAX(1,0,2)(2,0,0)(12) models were combined with the BSI. The 3 models had a good fit and passed the residuals Ljung-Box test. The ARIMA(4,0,0)(0,1,2)(12), ARIMA(4,0,0)(0,1,2)(12) + CSI (Lag=0), and ARIMAX(1,0,2)(2,0,0)(12) models demonstrated favorable predictive capabilities, with mean absolute errors of 1692.16 (95% CI 584.88-2799.44), 1067.89 (95% CI 402.02-1733.76), and 639.75 (95% CI 188.12-1091.38), respectively; root mean squared errors of 2036.92 (95% CI 929.64-3144.20), 1224.92 (95% CI 559.04-1890.79), and 830.80 (95% CI 379.17-1282.43), respectively; and mean absolute percentage errors of 4.33% (95% CI 0.54%-8.13%), 3.36% (95% CI -0.24% to 6.96%), and 2.16% (95% CI -0.69% to 5.00%), respectively. The ARIMAX models outperformed the ARIMA models and had better prediction performances with smaller values. This study demonstrated that the BSI can be used for the early warning and prediction of scarlet fever, serving as a valuable supplement to traditional surveillance systems.

Electrical consumption forecasting in sports venues: A proposed approach based on neural networks and ARIMAX Models

Building energy demand accounts for an important part of global energy consumption and emissions. Load forecasting is a crucial functionality for building management systems (BMSs) in order to optimize the energy supply and use with the goal of achieving economic and environmental benefits. Furthermore, if the building is a sports/event venue (one of the most energy-intensive facility type), load forecasting acquires an even greater importance.This paper presents a new forecasting hybrid technique that combines an ensemble of Artificial Neural Networks (ANNs) with an AutoRegressive Integrated Moving Average with eXternal inputs (ARIMAX) model. The proposed methodology is designed for the 24-hour-ahead electrical consumption forecasting of sports venues. These types of buildings are generally characterized by large fluctuations in their load caused by the hosted events, and therefore they are challenging for forecasting tools. The ANNs ensemble is composed of the average output of several multi-layer perceptrons, while the ARIMAX is used to correct the prediction provided by the ANNs working on the ensemble residual. The proposed approach can be easily implemented within a BMS that can be crucial for an efficient facility management. The hybrid methodology has been tested and validated on the load data of a real football arena. The proposed methodology achieves a MAPE of 9.03 % on an open dataset, and 9.38% on a closed dataset, outperforming standard machine learning techniques, hybridization techniques involving time-series and machine learning methods, as well as a state of the art event venue load forecasting technique. Furthermore, a feature importance study reveals that all the selected inputs are crucial in achieving the result. Finally, the inclusion of results related to an open dataset encourages further studies and comparisons.

Forecasting Emissions of Carbon Dioxide (CO2), Methane (CH4) and Energy Consumption in Egypt Using VECM and ARIMAX Models

Forecasting Emissions of Carbon Dioxide (CO2), Methane (CH4) and Energy Consumption in Egypt Using VECM and ARIMAX Models

Paving the way to the future: Mapping historical patterns and future trends of road material stock in Japan

Roads are a fundamental component of societal infrastructure, whose decades-long lifespan has far-reaching implications for developmental decisions. The road construction and development have profound impacts on economic growth, social dynamics, and environmental sustainability. Therefore, comprehensive measurement of the current road material stock (MS) and the projection of expected future road scale based on regional socio-economic scenarios that can reflect unique local conditions are necessary. This study examined the historical changes and progression patterns of the road network across Japan from 1965 to 2020 through material flow and material stock analysis. By using the road MS time series, along with explanatory socioeconomic variables, several models including Autoregressive Integrated Moving Average with explanatory variables (ARIMAX), Support Vector Regression (SVR), hybrid ARIMAX-SVR, Multiple Linear Regression (MLR), Artificial Neural Networks (ANN), and Random Forest (RF) were compared. After comparison analysis, ARIMAX and hybrid ARIMAX-SVR models were employed to forecast expected road MS in each prefecture of Japan by 2050 based on national shared socioeconomic pathways (SSP) scenarios. The study found that the total road MS of Japan increased 5.5-fold over 55 years. Aggregate was the dominant material, comprising over 70 % among the four materials of the total road MS. The forecast results for each prefecture were classified into three different patterns. Expected MS in most prefectures still displayed increasing trends in the five scenarios, but the projection of road MS in eight prefectures revealed a notable downward trend across each SSP scenario. For most prefectures, SSP5 displayed the highest expected road MS, followed by SSP1. SSP3 was the scenario with the lowest MS. This approach provided a more thorough understanding of the likely evolution of road MS across different SSP scenarios and could help inform decisions for resource allocation and policy formulation concerning road infrastructure management.

Forecasting the Fuel Consumption and Price for a Future Pandemic Outbreak: A Case Study in the USA under COVID-19

The COVID-19 epidemic and the measures adopted to contain it have had a significant impact on energy patterns throughout the world. The pandemic and movement restrictions led to unpredictable fluctuations in power systems demand and the fuel price for a delayed period. Monkeypox, another viral disease, appeared during the post-COVID period. It is assumed that the outbreak of monkeypox is unlikely due to the implication of preventive measures experienced from COVID-19. At the same time, the probability of an epidemic cannot be blindly overlooked. This paper aims to examine and analyze historical data to look at how much petroleum fuel was used for generating power and how the price of petroleum fuel changed over seven years, from January 2016 to August 2022. This period covers the time before the COVID-19 pandemic, during the pandemic, and after the pandemic. Several time-series forecasting models, including all four benchmark methods (Mean, Naive, Drift, and Snaive), Seasonal and Trend decomposition using Loess (STL), Exponential Smoothing (ETS), and Autoregressive Integrated Moving Average (ARIMA) methods have been applied for both fuel consumption and price prediction. The best forecasting method for fuel price and consumption has been identified among these methods. The best forecasting method for fuel consumption observed is ETS based on the RMSE value, which is 799.59, and the ARIMA method for fuel price, with RMSE 4.67. The paper also utilizes the ARIMAX model by incorporating multiple exogenous variables, such as monthly mean temperature, mean fuel price, and mileage of vehicles traveling during a certain period of pandemic lock-down. It will assist in capturing the non-smooth and stochastic pattern of fuel consumption and price due to the pandemic by separating the seasonal influence and, thus, provide a prediction of the consumption pattern in the event of any future pandemic. The novelty of the article will assist in exploring the potential energy demand in terms of cost and consumption of fuel during any pandemic period, considering the associated abnormalities.

HISTORICAL STRESS TEST OF CREDIT RISK USING MONTECARLO SIMULATION: INDONESIA ISLAMIC BANKING

This study was conducted to assess the vulnerability of Islamic banking credit portfolios under macroeconomic shocks and their impact on NPFs, using the linear regression analysis. From the three proposed models, namely OLS, ARIMAX and Error Correction Model (ECM), the ECM model was selected as the best. By using the Montecarlo simulation, an estimate was made of the possible future value of the NPF in Islamic banking. The study established that exchange rates, economic disparities, inflation, economic growth and interest rates showed positive and significant effects on bank credit risk whereas inflation produced negative effects. The non-performing finance (NPF) was also estimated using the Montecarlo simulation using one million trials. The results were subsequently used to conduct a stress test for the projected NPF. With 99% confidence, the maximum potential value of bad credit was 21%. The maximum NPF bad credit was attained under confidence level of 95%.

An Interpretable Time Series Data Prediction Framework for Severe Accidents in Nuclear Power Plants.

Accurately predicting severe accident data in nuclear power plants is of utmost importance for ensuring their safety and reliability. However, existing methods often lack interpretability, thereby limiting their utility in decision making. In this paper, we present an interpretable framework, called GRUS, for forecasting severe accident data in nuclear power plants. Our approach combines the GRU model with SHAP analysis, enabling accurate predictions and offering valuable insights into the underlying mechanisms. To begin, we preprocess the data and extract temporal features. Subsequently, we employ the GRU model to generate preliminary predictions. To enhance the interpretability of our framework, we leverage SHAP analysis to assess the contributions of different features and develop a deeper understanding of their impact on the predictions. Finally, we retrain the GRU model using the selected dataset. Through extensive experimentation utilizing breach data from MSLB accidents and LOCAs, we demonstrate the superior performance of our GRUS framework compared to the mainstream GRU, LSTM, and ARIMAX models. Our framework effectively forecasts trends in core parameters during severe accidents, thereby bolstering decision-making capabilities and enabling more effective emergency response strategies in nuclear power plants.

Uncertainty quantification of a deep learning model for failure rate prediction of water distribution networks

Predicting the time-dependent pipe failure rate of the water distribution networks (WDNs) is important for planning its renewal budget but also challenging due to the complex factors involved. The recent development of machine learning techniques provides a novel approach for accurate failure rate prediction based on historical data. However, the inherited randomness and uncertainty of water pipe failures and machine learning algorithms are often ignored in the training and prediction process. This article develops an uncertainty quantification framework for deep learning-based WDN system-wide failure rate prediction. The framework integrates a probabilistic long short-term memory (LSTM) model with a Monte Carlo method. The historical climate data and WDN pipe maintenance data over the past 35 years for Cuyahoga County, USA, are used to illustrate this deep-learning model-based uncertainty quantification framework in this study. A statistical time series regression model, ARIMAX, is used as a comparison benchmark. The results show that the LSTM model outperforms the ARIMAX model in prediction accuracy in most years by considering the uncertainties. Besides, the uncertainty range of the LSTM model prediction is 50% of that of the ARIMAX model. The results also identified the major contributing factors to the uncertainties of LSTM machine learning model prediction. The proposed uncertainty framework features excellent extensibility and can be adapted to quantify uncertainties with other types of machine learning models.