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Background: The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centrepiece in evidence-based disease management. Numerous approaches that use mathematical modelling have been used to predict the outcome of the pandemic, including data-driven models, empirical and hybrid models. This study was aimed at prediction of COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Material and Methods: Real-time Indian data of cumulative cases and deaths of COVID-19 was retrieved from the Johns Hopkins dashboard. The dataset from 11 March 2020 to 25 June 2020 (n = 107 time points) was used to fit the autoregressive integrated moving average model. The model with minimum Akaike Information Criteria was used for forecasting. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) were used to validate the model. Results: The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths, respectively, with minimum Akaike Information Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 June 2020 to 5 July 2020 showed a trend toward continuous increment. The PredRMSE and BaseRMSE of ARIMA (1,3,2) model were 21,137 and 166,330, respectively. Similarly, PredRMSE and BaseRMSE of ARIMA (3,3,1) model were 668.7 and 5,431, respectively. Conclusion: It is proposed that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimisation and evidence-based decision making for a subsequent state of affairs.

Pulses are staple protein-rich food for Indian vegetarians, and India is one of the largest producers in the world. The present investigation is an attempt to study the trend in the production of total pulses in India using the autoregressive integrated moving average (ARIMA) method. For stochastic trend estimation, yearly data were used for the period from 1961 to 2019. On the basis of the performance of several goodness of model fit criteria, the most suitable ARIMA model is chosen to capture the trend of pulse production. Forecasting for the 10 years from 2020 to 2029 is done, and it is observed that India has the highest forecast value (31.03302 million tonnes) in 2029. This study will play an important role in determining the gap between production of and demand for pulses in the future.

Preserving the air quality in urban areas is crucial for the health of the population as well as for the environment. The availability of large volumes of measurement data on the concentrations of air pollutants enables their analysis and modelling to establish trends and dependencies in order to forecast and prevent future pollution. This study proposes a new approach for modelling air pollutants data using the powerful machine learning method Random Forest (RF) and Auto-Regressive Integrated Moving Average (ARIMA) methodology. Initially, a RF model of the pollutant is built and analysed in relation to the meteorological variables. This model is then corrected through subsequent modelling of its residuals using the univariate ARIMA. The approach is demonstrated for hourly data on seven air pollutants (O3, NOx, NO, NO2, CO, SO2, PM10) in the town of Dimitrovgrad, Bulgaria over 9 years and 3 months. Six meteorological and three time variables are used as predictors. High-performance models are obtained explaining the data with R2 = 90%-98%.

Solar radiation is an important parameter in the study of electricity and/or thermal system installation. Direct monthly irradiance data measurements of the earth’s horizontal surface irradiance for the year 1985 to 2019 for nine stations (Sokoto, Birnin Kebbi, Maiduguri, Ilorin, Calabar, Port-Harcourt, Enugu, Iwo, and Ikeja) were collected from the achieve of HelioClim satellite website. The stationarity of the series was determined using the time plot of the irradiance data between the periods under study but the formal test of stationarity was carried out using the Augmented Dickey-Fuller (ADF) test with a p-value less than 0.05 signifying stationarity. Different autoregressive integrated moving average (ARIMA) models were fitted to the irradiance data for each of the selected stations. The results revealed that Sokoto, Maiduguri, and Kebbi have their maximum solar irradiance at about and their minimum at about , while Ilorin has its maximum solar irradiance at about and its minimum solar irradiance at about . Ikeja, Iwo and Port-Harcourt, Calabar and Enugu have their maximum solar irradiance and their minimum solar irradiance at about . It is therefore concluded that for Kebbi, Iwo and Maiduguri, the best model was ARIMA (3,0,3), for Calabar and Sokoto, it was ARIMA (2,0,2) while for other locations like Enugu, Ikeja, Ilorin and Port Harcourt, ARIMA (2,0,3) was the best model for forecasting irradiance in these study areas. The forecasted values of irradiance between January till December 2020 with its corresponding 95% confidence levels indicate good prediction of solar irradiance for future occurrence.

Background and Aim: The aim of this study is to analyse and forecast the coronavirus disease 2019 (COVID-19) cases in India, which may help the government and residents of India to mitigate the effect of COVID-19. Material and Methods: Univariate time series modelling has been used to forecast COVID-19 cases in India. The model is built to predict the number of confirmed cases, recovered cases, and death cases based on the data available between 30th June, 2020 and 28th August, 2020. Behaviour of the forecasts has been discussed at each round and also compared with the original values with its error measures such as mean absolute percentage error and root mean squared error. Results: For both the models used for fitting, namely exponential smoothing with multiplicative error and multiplicative trend, the later is found to be more appropriate than others. More importantly, at all stages of the forecast, the overall forecast error was

Air pollution seriously damages human health on a large scale. Although earlier works have improved a variety of predictive models of air pollution, the ability to accurately predict air pollution indices remains elusive. Time series prediction plays an important role in many fields. Some predecessors have experimented with artificial neural networks (NNs), combining linear autoregressive integrated moving average (ARIMA) models with nonlinear NN models. The typical assumption is that time series has a long signal with no white noise. However, a real-time short signal with white noise is common. The methods in the literature also do not guarantee that the prediction error of an NN model is minimized. Therefore, we propose the use of reinforcement learning (RL) to predict future PM2.5 values. First, we use the Q-learning algorithm in RL based on its state characteristics on the NN model. Second, we select the input with different input dimensions and values of time delay, calculate the best strategy, and evaluate the computational complexity of our RL algorithm. Finally, we show that we effectively reduce the prediction error of the NN models.

Soil temperature (Ts) plays an important role in earth sciences. The temporal and spatial variations of Ts are affected by several factors. To acquire the deterministic component and the stochastic component of time series data and further improve estimation performance, a multichanne long short-term memory model (MC-LSTM) is developed to address the challenge of accurate Ts estimation. Specifically, we connect multiple channels in an LSTM structure and an autoregressive integrated moving average model (ARIMA) directly to the output layer to characterize the deterministic part and the stochastic component of time series data. Meanwhile, to improve the correlations in the time series by increasing the number of memory units, we also connect the units in the different steps directly to the output layer in the LSTM structure to learn the long-term pattern of historical Ts by connecting the unit in an earlier step directly to the output layer, to learn the features that occurred in a short-term pattern by connecting the unit to the later step captures. In this article we test the hypothesis that Ts estimation is performed at depths of 5, 10 and 15 cm over 6, 12, and 24 hours. To achieve this, the input data are composed of half-hour data from the Ts of two synoptic stations (Laegern and Fluehli) in Switzerland. Meanwhile, the estimation accuracy is verified by three performance criteria; RMSE, MAE and R2. As expected, the proposed model achieves the highest relative R2 values of 0.9965 and the lowest values of RMSE = 0.3414 and MAE = 0.2310 for Ts estimation over 6 hours at Fluehli Station (10 cm soil depth) when compared with other state-of-the-art machine learning methods. Consequently, the proposed model can serve as an alternative approach for Ts profile estimation.

Applying a successful prediction of the confirmed, recovered and deaths is thought to be the basic requirement to successfully control the spreading rate of diseases. Time series models have extensively been considered as the suitable methods to forecast the confirmed, recovered and deaths because of the virus. The aim of this research is to apply the Autoregressive Integrated Moving Average (ARIMA) modelling approach for projectingCOVID-19 confirmed, recovered and deaths cases in Ethiopia. Over strict follows of all phases of Box-Jenkins strategy, ARIMA (1,1,1), ARIMA (16,1,2), and ARIMA (0,1,1)models for confirmed case, recovered and death case, respectively were selected as the best models for predicting coronavirus cases of Ethiopia. Using these models, a forecast of five-month a heads future situation of COVID-19 confirmed case, recovered and death case (Jan 3, 2021 to May 3, 2021) has been made. The results showed that in the coming five months from Jan 3, 2020 to May 3, 2021, the number of COVID-19 confirmed, recovered and deaths cases in Ethiopia may reach up to 320,597; 168,912 and 4438, respectively. Generally, the size of the coronavirus distribution was increased from time to time in the past ten months, until 3rd Jan, 2021, and it is expected to continue quicker than before for the coming 5-months, until the end of May, 2021, in Ethiopia and more rapidly than before while the peak will remain unknown yet. Therefore, effective implementation of the preventive measures and a rigorous compliance by avoiding negligence with the rules such as prohibiting public gatherings, travel restrictions, personal protection measures, and social distancing may improve the spreading rates of the virus. Further, through updating more new data with a continuous reconsideration of predictive model provide useful and more precise prediction

Soil moisture time series data are usually nonlinear in nature and are influenced by multiple environmental factors. The traditional autoregressive integrated moving average (ARIMA) method has high prediction accuracy but is only suitable for linear problems and only predicts data with a single column of time series. The gated recurrent unit neural network (GRU) can achieve the prediction of time series and nonlinear multivariate data, but a single nonlinear model does not yield optimal results. Therefore, a hybrid time series prediction model, BAG, combining linear and nonlinear characteristics of soil moisture, is proposed in this paper to achieve the identification process of linear and nonlinear relationships in soil moisture data so as to improve the accuracy of prediction results. In BAG, block Hankel tensor ARIMA (BHT-ARIMA) and GRU are selected to extract the linear and nonlinear features of soil moisture data, respectively. BHT-ARIMA is applied to predict the linear part of the soil moisture, and GRU is used to predict the residual series, which is the nonlinear part, and the superposition of the two predicted results is the final prediction result. The performance of the proposed model on five real datasets was evaluated. The results of the experiments show that BAG has a higher prediction accuracy compared with other prediction models for different amounts of data and different numbers of environmental factors.

Stock market prediction is considered a challenging task of financial time series analysis, which is beneficial for investors, stock traders, and future researchers. In Malaysia, many machine learning techniques have been used for stock price prediction such as Autoregressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN), and Long Short-Term Memory Network (LSTM). This study will use ARIMA and Seasonal ARIMA to present weekly, monthly and quarterly predictions, both with and without seasonal adjustment method. Stock movement prediction techniques are presented using weekly data of six industries in Malaysia such as gloves, property, airlines, banking, oil and gas, and pharmaceuticals from 26th September 2016 until 28th September 2020. The principle objective of this study is to verify seasonal and non-seasonal occurs in the Malaysian stock market and demonstrate the improvement in predictive performance of the stock market.

In this study, we focus on the analysis and prediction of urban logistics traffic flow, a field that is gaining increasing attention due to the acceleration of global urbanization and heightened environmental awareness. Existing forecasting methods face challenges in processing large and complex datasets, particularly when extracting and analyzing valid information from these data, often hindered by noise and outliers. In this context, time series analysis, as a key technique for predicting future trends, becomes crucial for supporting real-time traffic management and long-term traffic planning. To this end, we propose a composite network model that integrates gated recurrent unit (GRU), autoregressive integrated moving average (ARIMA), and temporal fusion transformer (TFT), namely the GRU-ARIMA-TFT network model, to enhance prediction accuracy and efficiency. Through the analysis of experimental results on different datasets, we demonstrate the significant advantages of this model in improving prediction accuracy and understanding complex traffic patterns. This research not only theoretically expands the boundaries of urban logistics traffic flow prediction but also holds substantial practical significance in real-world applications, especially in optimizing urban traffic planning and logistics distribution strategies during peak periods and under complex traffic conditions. Our study provides a robust tool for addressing real-world issues in the urban logistics domain and offers new perspectives and methodologies for future urban traffic management and logistics system planning., Competing Interests: Declaration of conflicting interestsThe author declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Introduction: Rabies is a fatal viral disease which is transmitted to humans through animal bites, most commonly via dogs. Fortunately, this disease is preventable through timely pre and postexposure vaccination. Aim: To study the seasonal predisposition and trend analysis of dog bite cases attending the anti rabies clinic. Materials and Methods: This retrospective cross-sectional study was conducted in the anti rabies clinic of a government hospital in Delhi. Enumeration and inclusion of all dog bite cases were made. An Autoregressive Integrated Moving Average (ARIMA) model was used to analyze the available data of dog bites, from 2011 to 2018. In this study, the least Bayesian Information Criterion (BIC) value was 12.2 and the corresponding model is ARIMA (1, 0, 0) with the goodness of fit 2 (R2 =44%). The model verification was done by noise residual check. The model was applied for time series analysis and forecasting of rabies cases in subsequent years. Results: Total number of dog bite cases were 1,46,344 in last eight years (2011-2018). The maximum number of cases being 27961 in the year 2014 followed by 22385 in the year 2013. A seasonal predisposition of dog bite cases was seen for the month of February to April. The trend analysis forecasting for 2019, 2020, 2021, 2022 and 2023 predicted 11317, 11676, 10157, 8639 and 7120 cases, respectively. Conclusion: Although the dog bite cases will be on a decline in the future, adequate measures need to be strengthened further to sensitise the community about rabies prevention and timely reporting to anti rabies clinic for prophylaxis.

Wind forecasting is a time series problem, can aide in estimating the annual energy production of potential wind farms. Seasonality and trend are the two significant components that characterize the wind time series data. Variability in trend and seasonal component affects the performance of most of the forecasting methods. Therefore, to simplify the wind forecasting technique, generally, nonlinear seasonal and trend components are eliminated from wind time series data. Accuracy depends on the application function that is applicable to eliminate the trend and seasonality. In this article, a hybrid approach for time series forecasting has been proposed. A clustering technique has been developed, which finds the clusters of time series data showing identical trend components. After finding the proper clusters of similar trend components, statistical methods, namely, autoregressive integrated moving average and generalized autoregressive score techniques, are applied to the individual cluster. In the end, resulting components are aggregated. The experiment shows that the cluster-based forecasting technique gives better performance as compared with existing statistical models. [ABSTRACT FROM AUTHOR]

Brucellosis has re‐emerged in China in recent years, resulting in an increasing health burden and economic losses for humans and the livestock industries. This study integrated data from human and livestock brucellosis surveillance systems to explore the changing epidemiology of brucellosis from 2004 to 2018 in China. A total of 524,980 human cases of brucellosis were reported, with the average annual incidence in humans being significantly higher for the period 2012–2018 than for 2004–2011 (3.3 vs. 1.9 per 100,000 residents). An autoregressive integrated moving average (ARIMA) model predicted an upward trend in the monthly incidence of brucellosis in humans in 2019 and 2020. Characteristics including being male, aged 45–54 years, working in the livestock industries, and residing in the northern provinces of China increased the risk of people contracting brucellosis. The percentage of provinces with infected people increased from 67.7% (21/31) in 2004 to all provinces in 2018. A total of 29,115 outbreaks were reported in livestock from 2004 to 2018, with 443,883 seropositive animals although only 381,224 (85.9%) of these were culled. The monthly incidence of brucellosis in humans was strongly positively correlated (r =.539, p <.001) with the number of outbreaks of brucellosis in livestock reported 3 months prior to the human cases. At the provincial level, the annual incidence of brucellosis in humans was significantly positively correlated with the sheep population (r =.786, p <.01). In conclusion, brucellosis in humans and livestock has been spreading in mainland China in the past decade. A more active surveillance of brucellosis in both livestock and humans in China should be coordinated and adjusted by adopting an evidence‐based 'One Health' approach, particularly in high‐risk regions and livestock industries. [ABSTRACT FROM AUTHOR]

Credit is an economic category and is also a product of the commodity economy, which exists through many socio-economic forms to promote economic growth. Therefore, the objective of this paper is to analyst, compare and forecast domestic credit growth in Vietnam's and China's economy. Thus, the paper is applied by a method of an autoregressive integrated moving average (ARIMA) model. This model is fitted to time series data both to better understand the data and to forecast future points in the series. Hereby, the methodology is selected by Vietnam's bestfit model ARIMA (2,3,1) and China's best-fit model ARIMA (2,3,5). Analytical data are collected from 1996 to 2017, the sample fitted the model and is statistically significant. The result show the forecast result for next years. The Vietnamese and Chinese economy are the open economies and have domestic credit greatly contributed to economic growth. These results are the basis for policymakers to have a general view and define the impact of domestic credit growth on GDP between the two countries.

In order to ensure the driving safety of vehicles in windy environments, a wind monitoring and warning system is widely used, in which a wind speed prediction algorithm with better stability and sufficient accuracy is one of the key factors to ensure the smooth operation of the system. In this paper, a novel short-term wind speed forecasting model, combining complementary ensemble empirical mode decomposition (CEEMD), auto-regressive integrated moving average (ARIMA), and support vector machine (SVM) technology, is proposed. Firstly, EMD and CEEMD are used to decompose the measured wind speed sequence into a finite number of intrinsic mode functions (IMFs) and a decomposed residual. Each of the IMF subseries has better linear characteristics. The ARIMA algorithm is adopted to predict each of the subseries. Then, a new subseries is reconstructed using the sum of the predicted errors of all subseries. The high nonlinear features of the reconstructed error subseries are modeled using SVM, which is suitable to process nonlinear data. Finally, the superposition of all prediction results is performed to obtain the final predicted wind speed. To verify the stability and accuracy of the model, two typhoon datasets, measured from the south coast of China, are used to test the proposed methods. The results show that the proposed hybrid model has a better predictive ability than single models and other combined models. The root mean squared errors (RMSEs) of the hybrid model for the three wind speed datasets are 0.839, 0.529, and 0.377, respectively. The combination of CEEMD with ARIMA contributes most of the prediction performance to the hybrid model. It is feasible to apply the hybrid model to wind speed prediction.

Regarding water consumption for various usage, water quality management is a significant part of water management. Future prediction of water quality parameters is necessary for the planning of water quality management. Sodium adsorption rate (SAR) is a parameter, which has a significant role in irrigation. In the present study, we compared the forecasting of SAR of water in Aras, Sepidrud, Karun, and Mond Rivers, Iran, using autoregressive integrated moving average (ARIMA) time series and radial basis functions (RBF) neural network. We found ARIMA (0,1,1) × (0,1,1)12, ARIMA (0,1,1) (0,1,1)12, ARIMA (0,1,1), and ARIMA (0,1,2) × (1,1,1)12 with minimum Akaike’s Information Criterion (AIC) of –1.7127, –1.8177, 2.9317, and 12.44 for SAR prediction of Aras, Sepidrud, Karun, and Mond Rivers, respectively. The residual of the mentioned ARIMA models was independent (p-value > 0.05). Using RBF neural network for SAR forecasting of Aras, Sepidrud, Karun, and Mond Rivers with normalized data, we obtained proper training and testing. Mean squared error (MSE are between 0.00026 and 0.0255) and mean bias error (MBE are between 0.01566 and 0.0612) for training is very low. Likewise, coefficient of determination (R² are between 0.907 and 0.960), index of agreement (IA are between 0.981 and 0.999), and the Nash–Sutcliffe efficiency (E are between 0.964 and 0.999) approach to 1, which describes the reliability of the model’s performance. Thirty-six months RBF neural network and 12 months of ARIMA time series of SAR forecasting for Aras River comparatively match to the measured data and forecast error of both RBF and ARIMA were comparable. We compared forecast errors of the ARIMA time series and RBF neural network for SAR forecasting of Sepidrud, Karun, and Mond Rivers; the results presented that RBF neural network is more reliable than ARIMA for the predicting of SAR. [ABSTRACT FROM AUTHOR]

Infectious Disease Prediction aims to anticipate the aspects of both seasonal epidemics and future pandemics. However, a single model will most likely not capture all the dataset’s patterns and qualities. Ensemble learning combines multiple models to obtain a single prediction that uses the qualities of each model. This study aims to develop a stacked ensemble model to accurately predict the future occurrences of infectious diseases viewed at some point in time as epidemics, namely, dengue, influenza, and tuberculosis. The main objective is to enhance the prediction performance of the proposed model by reducing prediction errors. Autoregressive integrated moving average, exponential smoothing, and neural network autoregression are applied to the disease dataset individually. The gradient boosting model combines the regress values of the above three statistical models to obtain an ensemble model. The results conclude that the forecasting precision of the proposed stacked ensemble model is better than that of the standard gradient boosting model. The ensemble model reduces the prediction errors, root-mean-square error, for the dengue, influenza, and tuberculosis dataset by approximately 30%, 24%, and 25%, respectively.

Introduction: Statistical methods to assess the impact of an intervention are increasingly used in clinical research settings. However, a comprehensive review of the methods geared toward practitioners is not yet available. Methods and Materials: We provide a comprehensive review of three methods to assess the impact of an intervention: difference-in-differences (DID), segmented regression of interrupted time series (ITS), and interventional autoregressive integrated moving average (ARIMA). We also compare the methods, and provide illustration of their use through three important healthcare-related applications. Results: In the first example, the DID estimate of the difference in health insurance coverage rates between expanded states and unexpanded states in the post-Medicaid expansion period compared to the pre-expansion period was 5.93 (95% CI, 3.99 to 7.89) percentage points. In the second example, a comparative segmented regression of ITS analysis showed that the mean imaging order appropriateness score in the emergency department at a tertiary care hospital exceeded that of the inpatient setting with a level change difference of 0.63 (95% CI, 0.53 to 0.73) and a trend change difference of 0.02 (95% CI, 0.01 to 0.03) after the introduction of a clinical decision support tool. In the third example, the results from an interventional ARIMA analysis show that numbers of creatinine clearance tests decreased significantly within months of the start of eGFR reporting, with a magnitude of drop equal to − 0.93 (95% CI, − 1.22 to − 0.64) tests per 100,000 adults and a rate of drop equal to 0.97 (95% CI, 0.95 to 0.99) tests per 100,000 per adults per month. Discussion: When choosing the appropriate method to model the intervention effect, it is necessary to consider the structure of the data, the study design, availability of an appropriate comparison group, sample size requirements, whether other interventions occur during the study window, and patterns in the data. [ABSTRACT FROM AUTHOR]

Countries throughout the world, including Indonesia, are facing a complex problem with regards to HIV infection incidences and its prevalence. This is despite some local governments in some provinces of Indonesia working together with the Social Ministry of Indonesia to eradicate prostitution. There are high numbers of HIV sub-types in Indonesia such as HIV-1 (CRF01_AE and B). The forecast was conducted with the Autoregressive Integrated Moving Average model. The ARMA (1,1) was observed to be the best model for forecasting the number of HIV patients in Indonesia. This forecasting has been done since March 2019. Based on its dynamic forecasting with ARMA (1,1), this study proved the number of HIV-positive patients, from 2019 to 2030, had increased from 22,679 to 36,255, by almost 37% within 12 years. Indonesia is facing a growing trend in the number of new HIV cases, until 2030 which caused by stopped their follow-up treatments or they have ceased in consuming the Antiretroviral drugs even though the Indonesian government was provided national health insurance which covers the Antiretroviral drug and a limited number of health-care services providing the Antiretroviral therapy. Therefore, investigations focusing on estimate the number of HIV patients in Indonesia is an important finding. The information can be used as a resource for policy and decision making for plans and programs. [ABSTRACT FROM AUTHOR]

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Improving the accuracy of public transport information has attracted attention in the development of smart cities. We aim to predict the bus travel speed on road sections using a long short-term memory (LSTM) neural network. We use digital tachograph (DTG) data combined with road link data. Motion sensors in DTG can record vehicle's operation information, such as journey distance, speed, and driving time. The experimental results show that the proposed model based on LSTM performs better than the autoregressive integrated moving average (ARIMA) model. The accuracy was improved by 20% on average. [ABSTRACT FROM AUTHOR]

Abstract Background Sepsis is an established global health priority with high mortality that can be curtailed through early recognition and intervention; as such, efforts to raise awareness are potentially impactful and increasingly common. We sought to characterize trends in the awareness of sepsis by examining temporal, geographic, and other changes in search engine utilization for sepsis information-seeking online. Methods Using time series analyses and mixed descriptive methods, we retrospectively analyzed publicly available global usage data reported by Google Trends (Google, Palo Alto, CA, USA) concerning web searches for the topic of sepsis between 24 June 2012 and 24 June 2017. Google Trends reports aggregated and de-identified usage data for its search products, including interest over time, interest by region, and details concerning the popularity of related queries where applicable. Outlying epochs of search activity were identified using autoregressive integrated moving average modeling with transfer functions. We then identified awareness campaigns and news media coverage that correlated with epochs of significantly heightened search activity. Results A second-order autoregressive model with transfer functions was specified following preliminary outlier analysis. Nineteen significant outlying epochs above the modeled baseline were identified in the final analysis that correlated with 14 awareness and news media events. Our model demonstrated that the baseline level of search activity increased in a nonlinear fashion. A recurrent cyclic increase in search volume beginning in 2012 was observed that correlates with World Sepsis Day. Numerous other awareness and media events were correlated with outlying epochs. The average worldwide search volume for sepsis was less than that of influenza, myocardial infarction, and stroke. Conclusions Analyzing aggregate search engine utilization data has promise as a mechanism to measure the impact of awareness efforts. Heightened information-seeking about sepsis occurs in close proximity to awareness events and relevant news media coverage. Future work should focus on validating this approach in other contexts and comparing its results to traditional methods of awareness campaign evaluation.

Prediction of currency exchange rate becomes highly desirable due to its greater role in financial and managerial decision making process. The fluctuations in exchange rate affect the economy of a country. Hence, over the years different types of neural network models along with statistical models are developed to predict the currency exchange rates of different countries with varying parameters. In this paper, we divide our effort into two parts. In first part, we have reviewed a few selected models of neural networks and statistics including fundamental and technical aspects of currency exchange rate prediction. Additionally, a thorough and careful experimental result analysis has been conducted on the models reviewed in part one. A committee machine has been proposed in part two to address the shortcomings of both neural networks and statistical models in the context of exchange rate prediction. Our study reveals that the currency exchange rates with multi-layer neural networks having Bayesian learning predictive accuracy is better than multi-layer neural networks with back-propagation learning. However, in the case of higher-order neural network multi-stage radial basis function network is predicting better than single stage radial basis function network. In the case of statistical models, it is drawn that under the umbrella of root mean square error measure, random walk is predicting better than other models of this category, whereas variance based model predicts better than rest of the models grouped under normalized mean square error measure. On the other hand, the integrated model is performing better than its counterpart like models with stand-alone mode. Moreover, our newly proposed committee machine is drawing a clear line over all the models while predicting exchange rate of GBP/USD. [ABSTRACT FROM AUTHOR]

Iron is one of the most applicable metals in the world. The global price of iron ore is determined based on demand and supply. There are numerous parameters (e.g., price of steel, steel production, oil price, gold price, interest rate, inflation rate, iron production, and aluminum price) affecting the global iron ore price. Considering the high number of effective parameters and existence of complex relationship among them, artificial intelligence-based approaches can be employed to predict iron ore price. In this paper, a new intelligence system namely group method of data handling (GMDH) was developed and introduced to predict the price of iron ore. For comparison purposes, four other techniques i.e., autoregressive integrated moving average (ARIMA), support vector regression (SVR), artificial neural network (ANN), and classification and regression tree (CART) were developed for prediction of monthly iron ore price. Then, using testing datasets, the developed models were validated and their performance capacities were compared. The results showed that performance prediction of the GMDH model is significantly better than other predictive models based on four performance indices i.e., root mean square error, variance account for (VAF), mean absolute error, and mean absolute percentage error. Results of VAF (97.89%, 90.81%, 80.95%, 55.02%, and 23.87% for GMDH, SVR, ANN, CART, and ARIMA models, respectively) revealed that the GMDH technique is able to predict iron ore price with higher degree of accuracy compared to the other techniques. [ABSTRACT FROM AUTHOR]

Nodes in a wireless sensor network are normally constrained by hardware and environmental conditions and face challenges of reduced computing capabilities and system security vulnerabilities. This fact calls for special requirements for network protocol design, security assessment models, and energy-efficient algorithms. Data aggregation is an effective energy conservation technique, which removes redundant information from the data aggregated from neighbor sensor nodes. How to further improve the effectiveness of data aggregation plays an important role in improving data collection accuracy and reducing the overall network energy consumption. Unfortunately, sensor nodes are normally deployed in an open environment and thus are subject to various attacks conducted by adversaries. Consequently, data aggregation brings new challenges to wireless sensor network security. In this article, we propose a novel secure data aggregation solution based on autoregressive integrated moving average model, a time series analysis technique, to prevent private data from being learned by adversaries. We leverage the autoregressive integrated moving average model to predict the data volume in sensor nodes, and update and synchronize the model as needed. The experimental results demonstrate that our model provides accurate predictions and that, compared with competing methods, our solution achieves better security, lower computation and communication costs, and better flexibility. [ABSTRACT FROM AUTHOR]

High prediction accuracies at time series modeling and forecasting is of the utmost importance for a variety of application domains. Many methods have been proposed in the literature to improve time series forecasting accuracy. Those which focus on univariate time series forecasting methods use only the values in the prior time steps to predict the next value. In this study in addition to the historical values, it is aimed to increase the forecasting performance by using extra statistical and structural features which summarize characteristics of the time series. Feature importance scores are determined by gradient boosting trees (GBT). Features with the highest importance score are given as explanatory additional variable to the hybrid ARIMA-ANN model. The evaluation of the developed method is performed on four different publicly available datasets. Our experimental results show higher accuracy performance for the proposed method as compared to the currently well-accepted methods. [ABSTRACT FROM AUTHOR]

Analysis of the rise in prices for consumer goods is a state’s priority task. The state assumes the obligation to regulate pricing in all spheres of consumption. First of all, the prices for essential commodities to which agricultural products belong are analyzed. The article shows the changes in prices for consumer goods of agricultural products (sugar) during a pandemic. The analysis of forecasting prices for sugar and its impact on the development of its production is carried out. The construction of the forecast model was based on extrapolation. The structure of a forecast model for price changes was based on the analysis of the time series of the Autoregressive Integrated Moving Average (ARIMA) class. This model consists of an autoregressive model and a moving average model. A forecast of the volume of domestic sugar transportation by rail has been completed. The algorithms implemented this model for searching for initial approximations and optimal parameters for the predictive model. The Hirotsugu Akaike Information Criterion (AIC) was used to select the best model. The algorithms were implemented in the Python programming language. The quality check of the description was performed with a predictive model of actual data. An economic interpretation of the rise in sugar prices and proof of the forecast’s truth obtained from a financial point of view were carried out.

Abstract Background Improving financial time series forecasting is one of the most challenging and vital issues facing numerous financial analysts and decision makers. Given its direct impact on related decisions, various attempts have been made to achieve more accurate and reliable forecasting results, of which the combining of individual models remains a widely applied approach. In general, individual models are combined under two main strategies: series and parallel. While it has been proven that these strategies can improve overall forecasting accuracy, the literature on time series forecasting remains vague on the choice of an appropriate strategy to generate a more accurate hybrid model. Methods Therefore, this study’s key aim is to evaluate the performance of series and parallel strategies to determine a more accurate one. Results Accordingly, the predictive capabilities of five hybrid models are constructed on the basis of series and parallel strategies compared with each other and with their base models to forecast stock price. To do so, autoregressive integrated moving average (ARIMA) and multilayer perceptrons (MLPs) are used to construct two series hybrid models, ARIMA-MLP and MLP-ARIMA, and three parallel hybrid models, simple average, linear regression, and genetic algorithm models. Conclusion The empirical forecasting results for two benchmark datasets, that is, the closing of the Shenzhen Integrated Index (SZII) and that of Standard and Poor’s 500 (S&P 500), indicate that although all hybrid models perform better than at least one of their individual components, the series combination strategy produces more accurate hybrid models for financial time series forecasting.

Abandoning the classical econometric modeling approach which consists in using explanatory variables (suggested by economic theory for prediction), we choose instead to use a sophisticated method developed by Box and Jenkins (1970) based solely on the past behavior of the variable being modeled/forecast. As we are in a data-rich environment and the economies and financial markets are more integrated than ever before, the quantitative methods in business and finance has increased substantially in recent years. This paper investigates the limitation of autoregressive integrated moving average (ARIMA) models in financial and monetary economics using the behavior of BET Index and EUR/RON exchange rates, respectively. Two important features discovered in the analysis of financial time series in this paper are fat-tails (large losses or gains are coming at a higher probability than the normal distribution would suggest) and volatility clustering, these empirical properties can’t be captured by integrated ARMA models, hence the limitation of these models.

Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus (SFTSV). Predicting the incidence of this disease in advance is crucial for policymakers to develop prevention and control strategies. In this study, we utilized historical incidence data of SFTS (2013-2020) in Shandong Province, China to establish three univariate prediction models based on two time-series forecasting algorithms Autoregressive Integrated Moving Average (ARIMA) and Prophet, as well as a special type of recurrent neural network Long Short-Term Memory (LSTM) algorithm. We then evaluated and compared the performance of these models. All three models demonstrated good predictive capabilities for SFTS cases, with the predicted results closely aligning with the actual cases. Among the models, the LSTM model exhibited the best fitting and prediction performance. It achieved the lowest values for mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). The number of SFTS cases in the subsequent 5 years in this area were also generated using this model. The LSTM model, being simple and practical, provides valuable information and data for assessing the potential risk of SFTS in advance. This information is crucial for the development of early warning systems and the formulation of effective prevention and control measures for SFTS., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

We introduce a comprehensive analysis of several approaches used in stock price forecasting, including statistical, machine learning, and deep learning models. The advantages and limitations of these models are discussed to provide an insight into stock price forecasting. Traditional statistical methods, such as the autoregressive integrated moving average and its variants, are recognized for their efficiency, but they also have some limitations in addressing non-linear problems and providing long-term forecasts. Machine learning approaches, including algorithms such as artificial neural networks and random forests, are praised for their ability to grasp non-linear information without depending on stochastic data or economic theory. Moreover, deep learning approaches, such as convolutional neural networks and recurrent neural networks, can deal with complex patterns in stock prices. Additionally, this study further investigates hybrid models, combining various approaches to explore their strengths and counterbalance individual weaknesses, thereby enhancing predictive accuracy. By presenting a detailed review of various studies and methods, this study illuminates the direction of stock price forecasting and highlights potential approaches for further studies refining the stock price forecasting models., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The increasing Russia-Ukraine crisis is without a doubt Europe's most prominent conflict since World War II, changing the dynamics of the oil and other key markets. Because the oil market has traditionally interacted with other financial and commodity markets, it will be intriguing to examine how it interacts with substantial financial assets amid market volatility induced by a conflict. The goal of this study is to propose a fuzzy time series (FTS) model and to compare its competitiveness to existing fuzzy time series (FTS) models, Autoregressive Integrated Moving Average (ARIMA) model and some machine learning methods i.e. Artificial Neural Networks (ANN), Support Vector Machine (SVM) and XGBoost models. We considered changes in the partitioning universe of discourse, optimization of parameters method(s), and interval estimation to make the forecast accuracy more precise forecasting than traditional methods via MAPE. The event-based data results show the proposed fuzzy time series model is outperforming all the competitive methods in the study. Furthermore, the proposed model forecasting shows a future decline tendency in WTi market crude oil prices (US$/BBL) after being at the record highest level, which is good news for the worldwide economy., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors. Published by Elsevier Ltd.)

Agriculture is the mainstay of Pakistan's economy and contributes 24 percent of the GDP. Considering its vital role, planners and policy makers are always keen to have timely forecasts for the important crops such as wheat, cotton, rice and sugarcane. Of these sugarcane is a major cash crop and an important source of income for farmers in Pakistan. The present study is focused on developing and estimating time series models to forecast sugarcane production in Pakistan. Box-Jenkin (1976) methodology was employed to estimate production forecasting model using annual time series data as available from Pakistan Bureau of Statistics (PBS) and various issues of Pakistan Economic Survey for the years 1947- 2017. An appropriate ARIMA (2. 1,1) model was estimated to forecast the production of sugarcane crop in Pakistan for the years 2018-2029. Over this period the model predicts a significant increase (6.56%) in sugarcane output. These forecasts can be very useful for agricultural policy makers, sugar industry as well as fanners in making prudent resource allocation and production decisions for sugarcane in Pakistan. [ABSTRACT FROM AUTHOR]

During machining processes, cutting temperature directly affects cutting performances, such as surface quality, dimensional precision, tool life, etc. Thus, evaluation of cutting temperature rise in the tool–chip interface by reliable techniques can lead to improved cutting performances. In this paper, we present the modeling of cutting temperature during facing process by using time series approach. The experimental data were collected by conducting facing experiments on a Computer Numerical Control lathe and by measuring the cutting temperature by an infrared camera. The collected data were used to test several Autoregressive Integrated Moving Average (ARIMA) models by using Box–Jenkins time series procedure. Then, the adequate model was selected according to four performance criteria: Akaike criterion, Schwarz Bayesian criterion, maximum likelihood, and standard error. The selected model corresponded to the ARIMA (1, 1, 1) and it was tested by conducting a new facing operation under the same cutting conditions (spindle speed, feed rate, depth of cut, and nose radius). It was clearly seen that there is a good agreement between experimental and simulated temperatures, which reveals that this approach simulates the evolution of cutting temperature in facing process with high accuracy (average percentage error = 0.57%). [ABSTRACT FROM AUTHOR]

A more widely used hybrid model of support vector regression (SVR) and autoregressive integrated moving average (ARIMA) based on Ensemble Empirical Mode Decomposition (EEMD) is proposed for failure time series prediction by taking advantage of the SVR model to forecast the nonlinear part of failure time series and the ARIMA model to predict the linear basic part. It firstly uses EEMD to decompose the original failure sequence into several significant fluctuation components and a trend component, and then it utilizes SVR and ARIMA to forecast them separately. The performance of the presented model is measured against other unitary models such as Holt- Winters, autoregressive integrated moving average, multiple linear regression, and group method of data handling of seven published nonlinear non-stationary failure datasets. The comparison results indicate that the proposed model outperforms other techniques and can be utilized as a promising tool for failure data forecast applications. [ABSTRACT FROM AUTHOR]

The onset, withdrawal and quantity of rainfall greatly influence the agricultural yield, economy, water resources, power generation and ecosystem. Time series modelling has been extensively used in stochastic hydrology for predicting various hydrological processes. The principles of stochastic processes have been increasingly and successfully applied in the past three decades to model many of the hydrological processes which are stochastic in nature. Time lagged models extract maximum possible information from the available record for forecasting. Artificial neural network has been found to be effective in modelling hydrological processes which are stochastic in nature. The ARIMA model was used to simulate and forecast rainfall using its linear approach and the performance of the model was compared with ANN. The computational approach of ANN is inspired from nervous system of living beings and the neurons possess the parallel distribution processing nature. ANN has proven to be a reliable tool for modelling compared to conventional methods like ARIMA and therefore ANN has been used in this study to estimate rainfall. In this study, rainfall estimation of Junagadh has been attempted using monthly rainfall training data of 32 years (1980-2011) and testing data of 5 years (2012-2016). A number of ANN model structures were tested, and the appropriate ANN model was selected based on its performance measures like root mean square error and correlation coefficient. The correlation coefficient Seasonal ARIMA (1,0,0)(3,1,1)12 and ANN back-propagation model (5- 12-1) on the testing data was found to be 0.75 and 0.79 respectively. Seasonal ARIMA (1,0,0)(3,1,1)12 and ANN back-propagation model (5-12-1) were used for forecasting rainfall of 5 years (2017-2021). [ABSTRACT FROM AUTHOR]

The sporadic and unstable nature of wind speed renders it very difficult to predict accurately to serve various decisions, such as safety in the air traffic flow and reliable power generation system. In this study we assessed the autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models on the wind speed time series problem. Data on wind speed and minimum and maximum temperatures were evaluated. Wind speed was established to follow a time series that fluctuated around ARIMA (0,1,1) and ARIMA (1,1,1). The optimal ANN model was established at 10 hidden neurons. The performance indices considered all indicated that the ANN wind speed model was superior to the ARIMA model. Wind speed prediction accuracy can be improved to secure the safety of air traffic flow as well support the implementation of a reliable and secure power generation system at the airport. [ABSTRACT FROM AUTHOR]

In conventional dam displacement monitoring models, forecast precision is below the standard, the fitting residual sequence contains chaotic components, and information mining of dam prototype observation data is limited. In consideration of the chaotic characteristics of the fitting residual sequence in regression model, the multi-scale wavelet analysis is used to decompose and reconstruct the residual sequence in this study; back propagation neural network and autoregressive integrated moving average model are used to forecast the reconstructed residual sequence by identifying the high-frequency and low-frequency characteristics of signals. By superimposing the residual forecast value with the forecast value of regression model, the combination forecast model for concrete dam displacement considering residual correction is proposed. Examples show that, compared with conventional models, the proposed combination model is better in fitting precision and convergence speed. Forecast capability is significantly improved for dam displacement forecast when effective components contained in residual sequence are considered. A new method of displacement forecast for high slope and other hydraulic structures is presented. [ABSTRACT FROM AUTHOR]