Adaptive Resource Scaling Algorithm for Serverless Computing Applications

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.

Providing fresh blood to keep people in need of blood alive, has always been a main issues of health systems. Right policy-making in this area requires accurate forecasting of blood demand. The current study aimed at predicting demand for different blood groups in Shiraz using Auto Regressive Integrated Moving Average (ARIMA), Artificial Neural Network (ANN) and a hybrid approaches. In the current time series analysis, monthly data of the Shiraz hospitals and medical centers demand for 8 blood groups during 2012–2019 were gathered from Shiraz branch of Iranian Blood Transfusion Organization. ARIMA, ANN and a hybrid model of them was used for prediction. To validate and comprise ARIMA and ANN models, Mean Square Error (MSE) and Mean Absolute Error (MAE) criteria were used. Finally, ARIMA, ANN and hybrid model estimates were compared to actual data for the last 12 months. R3.6.3 were used for statistical analysis. Based on the MSE and MAE of models, ARIMA had the best prediction for demand of all blood groups except O+ and O−. Moreover, for most blood groups, ARIMA had closer prediction to actual data. The demand for four blood groups (mostly negative groups) was increasing and the demand for other four blood groups (mostly positive ones) was decreasing. All three approaches including ARIMA, ANN and the hybrid of them predicted an almost downward trend for the total blood demand. Differences in the performance of various models could be due to the reasons such as different forecast horizons, daily/month/annual data, different sample sizes, types of demand variables and the transformation applied on them, and finally different blood demand behaviors in communities. Advances in surgical techniques, fetal screening, reduction of accidents leading to heavy bleeding, and the modified pattern of blood request for surgeries appeared to have been effective in reducing the demand trend in the current study. However, a longer time period would certainly provide more accurate estimates.

In the era of sustainable finance, Environmental, Social, and Governance (ESG) factors have emerged as key indicators influencing market behavior and investor sentiment. This study presents a novel hybrid framework that integrates Long Short-Term Memory (LSTM) networks and Auto-Regressive Integrated Moving Average (ARIMA) models to forecast financial market volatility and risk while incorporating ESG signals. The proposed deep statistical fusion model leverages the strengths of ARIMA in capturing linear temporal dependencies and LSTM’s ability to model complex nonlinear patterns from sequential data. ESG scores, along with historical price movements and macroeconomic indicators, are used as primary inputs to enhance model sensitivity to sustainability-related risk. Experiments were conducted using real-world datasets from global stock indices (e.g., NSE, S&P 500) and third-party ESG rating providers. The model's performance was assessed using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and volatility clustering evaluation. The hybrid LSTM-ARIMA model achieved an RMSE of 1.92 and MAPE of 3.85%, outperforming standalone ARIMA (RMSE: 3.14, MAPE: 6.42%) and LSTM (RMSE: 2.41, MAPE: 5.12%). Additionally, the proposed model demonstrated better risk sensitivity by accurately flagging high-volatility periods linked to ESG controversies and macroeconomic disruptions. These results confirm that incorporating ESG factors within a deep statistical fusion framework enhances forecasting precision, offering a robust tool for financial institutions and ESG-conscious investors in proactive risk management and strategic decisionmaking.

The aspiration to predict the future values as close to the actual values as possible leads to the invention of time series models, the autoregressive integrated moving average (ARIMA) model which requires appropriate parameters of model identification, the ARIMA order, prior to fit coefficients of the models using the Box-Jenkins method. Statisticians for a decade identified the order via the sample autocorrelation function (ACF) and the sample partial autocorrelation function (PACF) which were very challenging for a human eye. To circumvent this issue, the recent model identification development uses a likelihood based-method that automatically generates orders and fits coefficients by varying the ARIMA order and pick the best one having the smallest Akaike information criterion (AIC) or Bayesian information criterion (BIC). The acquired ARIMA model may fail residual diagnostics. Consequently, this paper proposes the convolution neural network model, called the self-identification deep learning (SID) model, to automatically identify the ARIMA order via sample ACF/PACF. Accordingly, randomly simulated time series data with stationary and invertibility properties are generated by ARIMA model. Next, the time series data are converted into ACF/PACF graphs in order to feed into the SID model. The derived ARIMA order will be passed to determine the best fit coefficients of the ARIMA model via the Box-Jenkin methods for forecasting future values. The complete algorithm is called the self-identification deep learning ARIMA (SIDA) algorithm. The performance of identifying the ARIMA order from the SID model outperforms the likelihood based-method and ResNET50 which accepts the time series data directly in terms of precision, recall and F1-scores. Moreover, the SIDA algorithm applying to the real world dataset shows a better performance over the likelihood-based method via the mean absolute percentage error, the symmetric mean absolute percentage error, the mean absolute error and the root mean square error.

Resource optimization is one of the keys in achieving established organization objectives. In making concrete goals, forecasting can be used. Forecasting the near future’s number of bull services (i.e. natural mating with a male purebred dairy-type carabao or technically known as carabull) may help the Philippine Carabao Center-Visayas State University (PCC-VSU) in its resource (e.g. manpower, financial, animal) target setting. This can assist in formulating livestock development plan in the genetic improvement program of PCC-VSU necessary to uplift the Philippine carabao industry; thus, can help to improve the lives of the farmers especially the smallholder carabao raisers. This study implemented a bull services Auto-Regressive Integrated Moving Average (ARIMA) timeseries forecasting which is one of the advanced forecasting models used to predict trend for the next few years. An appropriate ARIMA model was selected from the analyzed bull services data from 2002 to 2014. Data from 2015 to 2017 were utilized as testing data and bull services for 2019 to 2021 were predicted as well. The results show that the ARIMA forecast are better than the existing annual target setting method used by the center. The model predicted values are generally closer to the actual number of bull services than the center’s targeted values. In terms of Mean Absolute Percent Error (MAPE), ARIMA scored 3.31. The development of a prognostic application to predict calf or young carabao gender is recommended, if applicable.

Introduction: Law enforcement agencies have struggled to forecast crime due to the complexity of criminal conduct and its dynamic elements. Regular developments and the growth of well-known and new platforms have made social media a pervasive way for people to express their ideas and experiences, popularizing blogging and democratizing information distribution. Traditional time series models like Autoregressive Integrated Moving Average (ARIMA) are frequently employed for forecasting, but they may struggle to capture crime data's nonlinear relationships and long-term dependencies. However, Long Short-Term Memory (LSTM) are best at catching sequential patterns but may struggle with short-term data or rapid criminal patterns. Combining strength of both methods may improve forecast accuracy and robustness. Objectives: To develop a hybrid ARIMA-LSTM model that effectively captures both linear temporal trends and complex nonlinear patterns in crime data, enhancing predictive accuracy across diverse crime types and geographic regions in India. Methods: The proposed fusion approach leverages the strengths of ARIMA in modelling temporal dependencies and the ability of LSTMs to capture complex nonlinear relationships. Initially, ARIMA is employed to model the underlying trend and seasonality in the crime data. Subsequently, the residuals obtained from ARIMA are fed into an LSTM architecture, such as LSTM, to capture the remaining nonlinear patterns and dependencies. The hybrid model is trained using historical crime data and validated using appropriate evaluation metrics. The performance of the proposed fusion approach is evaluated on real-world crime datasets across various geographic locations and crime types in India. Results: Experimental results demonstrate that the hybrid ARIMA-LSTM model outperforms individual methodologies and baseline models in terms of MSE, RMSE, and accuracy. Conclusions: This research contributes to the development of more effective crime prediction models, aiding law enforcement agencies in proactive decision-making and resource allocation for crime prevention.

Accurate forecasting of weather and climate variables is fundamental to environmental monitoring, disaster preparedness, agricultural planning, and sustainable resource management. The inherent complexity, nonlinearity, and temporal variability of meteorological time series present significant challenges to traditional forecasting techniques. While statistical models such as Autoregressive Integrated Moving Average (ARIMA) are proficient in modeling linear trends and seasonal components, they are limited in capturing nonlinear dependencies. Conversely, Long Short-Term Memory (LSTM) networks have demonstrated superior performance in learning long term temporal correlations and nonlinear structures in sequential data. This book chapter introduces a hybrid ARIMA-LSTM framework that integrates the strengths of both paradigms to enhance the predictive accuracy and robustness of weather and climate forecasts. The proposed methodology involves decomposing the time series into linear and nonlinear components, with ARIMA modeling the deterministic trends and LSTM capturing the residual nonlinear dynamics. Extensive experiments conducted on real-world meteorological datasets—including temperature, precipitation, and humidity—demonstrate that the hybrid model consistently outperforms standalone ARIMA and LSTM models in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). The results confirm the hybrid model’s superior capability in handling complex temporal patterns, particularly under nonstationary conditions and extreme variability.

Emerging economy strives for advanced manufacturing practices including waste management, resource optimisation and zero gas emissions. This study seeks to investigate and provide solutions enabling circular economy manufacturing in electricity production in South Africa. The Autoregressive integrated moving average (ARIMA) model was applied to predict the 2030 circular values using observation from 2017 to 2023. The Ljung-Box Q coefficients indicate the fitness of the time series models in comparison with the chi-square. Forecasting analysis highlighted a decrease in resource consumption and greenhouse gas emissions from 2024 to 2030 in alignment with the United Nations Sustainable Development Goals compared to an increase in waste generation. However, the target of reaching zero emissions in 2030 is far from realistic given ARIMA models. Eskom should fully adopt renewable energy sources like solar, wind and geothermal compared to fossil fuels for effective resource efficiency, zero greenhouse gas emissions and circular waste management to meet global requirements on pollution, climate change and finite resources preservation. The South African Government should update water infrastructure and regulations for technological innovation to achieve the circularity expectations without compromising the social corporate responsibility for inclusive growth.

Evapotranspiration is the one of the major role playing element in water cycle. More accurate measurement and forecasting of Evapotranspiration would enable more efficient water resources management. This study, is therefore, particularly focused on evapotranspiration modelling and forecasting, since forecasting would provide better information for optimal water resources management. There are numerous techniques of evapotranspiration forecasting that include autoregressive (AR) and moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), Thomas Feiring, etc. Out of these models ARIMA model has been found to be more suitable for analysis and forecasting of hydrological events. Therefore, in this study ARIMA models have been used for forecasting of mean monthly reference crop evapotranspiration by stochastic analysis. The data series of 102 years i.e. 1224 months of Bokaro District were used for analysis and forecasting. Different order of ARIMA model was selected on the basis of autocorrelation function (ACF) and partial autocorrelation (PACF) of data series. Maximum likelihood method was used for determining the parameters of the models. To see the statistical parameter of model, best fitted model is ARIMA (0, 1, 4) (0, 1, 1)12.

With the development of Information and Communication Technology (ICT), the service provided by cloud data centers has become a new pattern of Internet services. The prediction of the number of arriving tasks plays a crucial role in resource allocation and optimization for cloud data center providers. This work proposes a hybrid method that combines wavelet decomposition and autoregressive integrated moving average (ARIMA) to predict it at the next time interval. In this approach, the task time series is smoothed by Savitzky-Golay filtering, and then the smoothed time series is decomposed into multiple components via wavelet decomposition. An ARIMA model is established for the statistical characteristics of the trend and components, respectively. Finally, their prediction results are reconstructed via wavelet reduction and the predicted number of arriving tasks is obtained. Experimental results demonstrate that the hybrid method achieves better prediction results compared with some typical prediction methods including ARIMA and neural networks.

This paper presents a multistage supply chain model that is based on Autoregressive Integrated Moving Average (ARIMA) time-series models. Given an ARIMA model of consumer demand and the lead times at each stage, it is shown that the orders and inventories at each stage are also ARIMA, and closed-form expressions for these models are given. The paper also discusses the causes of the bullwhip effect, a phenomenon in which variation in demand produces larger variations in upstream orders and inventory. This discussion reveals how different modeling can lead to different insights because they make different assumptions about the cause of the bullwhip effect. These observations are used to develop managerial insights about reducing the bullwhip effect.

Efficient allocation of hospital resources is essential for ensuring timely and equitable healthcare delivery, particularly during periods of fluctuating patient demand such as pandemics, seasonal disease outbreaks, or disaster scenarios. This study presents a hybrid approach combining time series analysis and machine learning models to forecast hospital resource demand, including bed occupancy, ICU capacity, staffing requirements, and medical supplies. By integrating historical admission data, disease incidence trends, demographic information, and external factors such as weather and public health interventions, the model enables healthcare administrators to anticipate resource needs with greater precision. The forecasting framework employs autoregressive integrated moving average (ARIMA) models to capture temporal patterns, seasonality, and autocorrelation in hospital usage data. In parallel, machine learning algorithms such as Random Forest, Gradient Boosting, and Long Short-Term Memory (LSTM) neural networks are used to model complex, nonlinear relationships and exogenous variable impacts. The ensemble method leverages the strengths of both statistical and machine learning approaches, enhancing forecast robustness and adaptability. The model is trained and validated using real-world datasets from national health services and regional hospitals, spanning both normal and surge conditions. Evaluation metrics including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are used to assess performance across different time horizons and resource types. Results show that the hybrid model significantly outperforms traditional single-method approaches in terms of forecast accuracy and responsiveness to sudden demand changes. This research provides a decision-support tool for proactive hospital resource management, facilitating dynamic planning and improving preparedness during crises. The model’s ability to generate interpretable and timely forecasts can assist hospital administrators, policymakers, and emergency response teams in optimizing staffing schedules, managing inventory, and minimizing care delays. The study advocates for the integration of advanced predictive analytics into hospital operations as a pathway to more resilient and data-driven healthcare systems. Keywords: Hospital Resource Forecasting, Time Series Analysis, Machine Learning, ARIMA, LSTM, Random Forest, Healthcare Planning, Surge Capacity, Predictive Analytics, Hospital Operations.

With the increasing importance of forecasting with the utmost degree of accuracy, utilizing hybrid frameworks become a must for obtaining more accurate and more reliable forecasting results. Series hybrid methodology is one of the most widely-used hybrid approaches that has encountered a great amount of popularity in the literature of time series forecasting and has been applied successfully in a wide variety of domains. In such hybrid methods is assumed that there is an additive relationship among different components of time series. Thus, based on this assumption, various individual models can apply separately on decomposed components, and the final forecast can be obtained. However, developed series hybrid models in the literature are constructed based on the decomposing time series into linear and nonlinear parts and generating linear-nonlinear modeling order for decomposed parts. Another assumption considered in the traditional series model is assigning equal weights to each model used for modeling linear and nonlinear components. Thus, contrary to traditional series hybrid models, to improve the performance of series hybrid models, these two basic assumptions have been violated in this paper. This study aims to propose a novel weighted MLP-ARIMA model filling the gap of series hybrid models by changing the order of sequence modeling and assigning weight for each component. Firstly, the modeling order is changed to nonlinear-linear, and then Multi-Layer Perceptron Neural Network (MLPNN) -Auto-Regressive Integrated Moving Average(ARIMA) models are employed to model and process nonlinear and linear components respectively. Secondly, each model's weights are computed by the Ordinary Least Square (OLS) weighting algorithm. Thus, in this paper, a novel improved weighted MLP-ARIMA series hybrid model is proposed for time series forecasting. The real-world benchmark data sets, including Wolf's sunspot data, the Canadian lynx data, and the British pound/US dollar exchange rate data, are elected to verify the effectiveness of the proposed weighted MLP-ARIMA series hybrid model. The simulation results revealed that the weighted MLP-ARIMA model could obtain superior performance compared to ARIMA-MLP, MLP-ARIMA, as well as the ARIMA and MLPNN individual models. The proposed hybrid model can be an effective alternative to improve forecasting accuracy obtained by traditional series hybrid methods.

Traffic signals as part of intelligent transportation systems can play a significant role in making cities smart. Conventionally, most traffic lights are designed with fixed-time control, which induces a lot of slack time (unused green time). Actuated traffic lights control traffic flow in real time and are more responsive to the variation of traffic demands. For an isolated signal, a family of time series models, such as autoregressive integrated moving average (ARIMA) models, can be beneficial for predicting the next cycle length. However, when there are multiple signals placed along a corridor with different spacing and configurations, the cycle length variation of such signals is not just related to each signal’s values, but it is also affected by the platoon of vehicles coming from neighboring intersections. In this paper, a multivariate time series model is developed to analyze the behavior of signal cycle lengths of multiple intersections placed along a corridor in a fully actuated setup. Five signalized intersections have been modeled along a corridor, with different spacing among them, together with multiple levels of traffic demand. To tackle the high-dimensional nature of the problem, a penalized least-squares method is utilized in the estimation procedure to output sparse models. Two proposed sparse time series methods captured the signal data reasonably well and outperformed the conventional vector autoregressive model—in some cases up to 17%—as well as being more powerful than univariate models, such as ARIMA.

Air pollution risk assessment is complex due to dynamic data change and pollution source distribution. Air quality index concentration level prediction is an effective method of protecting public health by providing the means for an early warning against harmful air pollution. However, air quality index-based prediction is challenging as it depends on several complicated factors resulting from dynamic nonlinear air quality time-series data, such as dynamic weather patterns and the verity and distribution of air pollution sources. Subsequently, some minimal models have incorporated a time series-based predicting air quality index at a global level (for a particular city or various cities). These models require interaction between the multiple air pollution sensing sources and additional parameters like wind direction and wind speed. The existing methods in predicting air quality index cannot handle short-term dependencies. These methods also mostly neglect the spatial correlations between the different parameters. Moreover, the assumption of selecting the most recent part of the air quality time series is not valid considering that pollution is cyclic behavior according to various events and conditions due to the high possibility of falling into the trap of local minimum and poor generalization. Therefore, this paper proposes a new air pollution global risk assessment (APGRA) prediction model for an air quality index of spatial correlations to address these issues. The APGRA model incorporates an autoregressive integrated moving average (ARIMA), a Monte Carlo simulation, a collaborative multi-agent system, and a prediction algorithm for reducing air quality index prediction error and processing time. The proposed APGRA model is evaluated based on Malaysia and China real-world air quality datasets. The proposed APGRA model improves the average root mean squared error by 41%, mean and absolute error by 47.10% compared with the conventional ARIMA and ANFIS models.