Search

Accurate wind speed forecasting is very crucial for wind power generation systems, but the inherent randomness of wind speeds makes wind speed forecasting challenging. There have been many studies on predicting wind speeds, but they ignored the influence factor of wind speed on its change over time with multiple factors, such as wind direction, temperature, humidity and atmospheric pressure. Therefore, a secondary factor induced wind speed time series prediction using self-adaptive interval type-2 fuzzy sets (IT2FS) with error correction was proposed. First, an IT2FS model is developed to induce secondary factors to predict wind speed. Specifically, the differential evolution algorithm is employed to optimize parameters of IT2FS model. Second, error correction strategy is adopted to correct the model error. The auto-regressive integrated moving average model is used to predict the residual sub-sequence after variational mode decomposition. Finally, by predicting the wind speed of two wind farms in China, it is verified that the proposed hybrid system transcends other compared models and simultaneously realizes high accuracy and strong stability. Thus, employing a new strategy to conduct the main factor time series prediction using its secondary factors is extremely useful for enhancing the prediction performance.

Uncertain time series analysis aims to explore how the current observation is affected by the disturbance terms and past imprecise observations characterized as uncertain variables. For the case th...

In this article, some linear predictors have been introduced for prediction in a first-order moving average process, $${\rm{M}}{\rm{A}}(1)$$ . Two comparison criteria, the Pitman’s measure of closeness and mean square error of prediction have been applied to find the best linear predictor in the introduced class. Estimation of parameters has been done by MLE method. As an illustrative example, the analysis of a real data set has also been performed.

The objective of this study is to develop a forecasting model for causal factors management in the future in to order to achieve sustainable development goals. This study applies a validity-based concept and the best model called “Path analysis based on vector autoregressive integrated moving average with observed variables” (Path Analysis-VARIMA-OVi Model). The main distinguishing feature of the proposed model is the highly efficient coverage capacity for different contexts and sectors. The model is developed to serve long-term forecasting (2020-2034). The results of this study show that all three latent variables (economic growth, social growth, and environmental growth) are causally related. Based on the Path Analysis-VARIMA-OVi Model, the best linear unbiased estimator (BLUE) is detected when the government stipulates a new scenario policy. This model presents the findings that if the government remains at the current future energy consumption levels during 2020 to 2034, constant with the smallest error correction mechanism, the future CO2 emission growth rate during 2020 to 2034 is found to increase at the reduced rate of 8.62% (2020/2034) or equivalent to 78.12 Mt CO2 Eq. (2020/2034), which is lower than a carrying capacity not exceeding 90.5 Mt CO2 Eq. (2020-2034). This outcome differs clearly when there is no stipulation of the above scenario. Future CO2 emission during 2020 to 2034 will increase at a rate of 40.32% or by 100.92 Mt CO2 Eq. (2020/2034). However, when applying the Path Analysis-VARIMA-OVi Model to assess the performance, the mean absolute percentage error (MAPE) is estimated at 1.09%, and the root mean square error (RMSE) is estimated at 1.55%. In comparison with other models, namely multiple regression model (MR model), artificial neural network model (ANN model), back-propagation neural network model (BP model), fuzzy analysis network process model (FANAP model), gray model (GM model), and gray-autoregressive integrated moving average model (GM-ARIMA model), the Path Analysis-VARIMA-OVi model is found to be the most suitable tool for a policy management and planning to achieve a sustainability for Thailand. Keywords: Sustainable Development, energy consumption, Managing Future Scenarios, Forecasting Model, Carrying Capacity.JEL Classifications: P28, Q42, Q43, Q47, Q48DOI: https://doi.org/10.32479/ijeep.9693

Activity classification has been an interesting area of research for many years, to better understand human behavior. Recent advancements in embedded computing systems allowed the emergence of several state-of-art solutions for human activity classification using sensors of a smartphone. The sensors generate temporal sequences of observations for human activity, which is called as Multivariate Time Series (MTS). Current state-of-art solutions for human activity classification suffer from two major limitations: first, the length of testing MTS should be equal to the training MTS and second, the MTS should not have any faulty time series. In real-time applications, it is desirable to classify a human activity using an incomplete MTS as early as possible. In this work, we propose a fault-tolerant early classification of MTS (FECM) approach to address these limitations. FECM builds a set of classification models using MTS training dataset. The approach employs Gaussian Process classifier to estimate minimum required length of time series, which is used to predict a class label of new MTS. Further, FECM uses an Auto Regressive Integrated Moving Average model to identify faulty time series in the new MTS. Finally, we conduct an experiment to evaluate the performance of FECM using accuracy and earliness metrics.

This study defines the best model for a chemical waste plant where the Artificial Neural Network (ANN) and the Integrated Auto Regressive Moving Average Model (ARIMA) were applied as tools for predicting future maintenance cost data. These methods were applied together considering the criteria as follows: plant size, process cost, treatment flexibility, environmental safety and maintenance cost. For this, a decision-making model was developed using the Hierarchical Analysis Method (AHP) with which the company can decide from three alternatives of waste plant models. As a result, the recommendation and solution provide by the multicriteria method was the choice of the alternative 3 of a waste center. This solution indicated the best alternative considering the criteria selected by the company and also the data from RNA and ARIMA In this case, the model presented an index above 70% both in the final aggregation and in the sensitivity analysis. http://dx.doi.org/10.18226/23185279.v9iss1p30

Objective: The aim of the present study was to compare the effectiveness of three approaches (the seasonal auto-regressive integrated moving average [SARIMA] model, the nonlinear autoregressive exogenous [NARX] artificial neural networks and Wood’s model) to the prediction of milk yield during lactation.Methods: The dataset comprised monthly test-day records from 965 Polish Holstein-Friesian Black-and-White primiparous cows. The milk yields from cows in their first lactation (from 5 to 305 days in milk) were used. Each lactation was divided into ten lactation stages of approximately 30 days. Two age groups and four calving seasons were distinguished. The records collected between 2009 and 2015 were used for model fitting and those from 2016 for the verification of predictive performance.Results: No significant differences between the predicted and the real values were found. The predictions generated by SARIMA were slightly more accurate, although they did not differ significantly from those produced by the NARX and Wood’s models. SARIMA had a slightly better performance, especially in the initial periods, whereas the NARX and Wood’s models in the later ones.Conclusion: The use of SARIMA was more time-consuming than that of NARX and Wood’s model. The application of the SARIMA, NARX and Wood’s models (after their implementation in a user-friendly software) may allow farmers to estimate milk yield of cows that begin production for the first time.

With the upsurge in restructuring of the power markets, wind power has become one of the key factors in power generation in the smart grids and gained momentum in the recent years. The accurate wind power forecasting is highly desirable for reduction of the reserve capability, enhancement in penetration of the wind power, stability and economic operation of the power system. The time series models are extensively used for the wind power forecasting. The model estimation in the ARIMA model is usually accomplished by maximizing the log likelihood function and it requires to be re-estimated for any change in input value. This degrades the performance of the ARIMA model. In the proposed work, the model estimation of the ARIMA model is done using latest evolutionary algorithm (i.e., dynamic particle swarm optimization [DPSO]). The use of DPSO algorithm eliminates the need for re-estimation of the model coefficients for any change in input value and moreover, it improves the performance of ARIMA model. The performance of proposed DPSO-ARIMA model has been compared to the existing models.

The genesis of novel coronavirus (COVID-19) was from Wuhan city, China in December 2019, which was later declared as a global pandemic in view of its exponential rise and spread around the world. Resultantly, the scientific and medical research communities around the globe geared up to curb its spread. In this manuscript, authors claim competence of AI-mediated methods to predict mortality rate. Efficient prediction model enables healthcare professionals to be well prepared to handle this unpredictable situation. The prime focus of the study is to investigate efficient prediction model. In order to determine the most effective prediction model, authors perform comparative analysis of numerous models. The performance of various prediction models is compared using various error metrics viz. Root mean square error, mean absolute error, mean square error and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${R}^{2}$$\end{document}R2. During comparative analysis, Auto seasonal auto regressive integrated moving average model proves its competence over comparative models.

Information criteria have been widely used in many quantitative applications as an effort to select the most appropriate model that describes well enough the unknown population behavior for a given dataset. Studies have shown that their performance depends on several elements and the selection of the best fitted model is not always the same for all criteria. For this purpose, this research evaluates the performance of the three most often used information criteria, such as the Akaike information criterion, the Bayesian information criterion and Hannan and Quinn information criterion, for selecting spatial processes, taking into account that the sample in spatial analysis is regarded as a realization of a spatial process that incorporates the spatial dependence between the observations. Using a Monte Carlo analysis for the three most frequently applied in practice spatial processes, such as the first-order spatial autoregressive process, SAR(1), the first-order spatial moving average process, SMA(1), and the mixed spatial autoregressive moving average process, SARMA(1, 1), this study finds that these information criteria can assist the analyst to select the true process, but their behavior depends on sample size as well as on the magnitude of the spatial parameters, leading occasionally to alternative competitive processes.

Stock prediction with data mining techniques is one of the interesting areas being investigated in recent research. Weighted Moving Average (WMA) technique is one such widely used technique in stock forecasting, in which each historical data term can have its own weightage. One of the main drawbacks of WMA is that there is no exact base to determine those weighting factors. Because of this drawback, the investors can assign arbitrary weightages on periodical data though it is misleading the investment decisions. The present study addresses the limitations in Weighted Moving Average technique and tries to generate more reliable and statistically proven weighting factors for stock price prediction. We develop a more reliable stock predictive model by using a panel data set of quarterly closing stock prices of 41 companies for a period of ten years, approximating a sample size of 1680 observations. The Auto Regressive Moving Average model analysed in our study provides strong evidence for statistically significant impact of past stock prices on current stock prices. The study further found statistically more reliable weight factors for past four quarters which can be used for forecasting future stock prices. The findings of the present study confirm that the weight factor drops as the data become older in a linear pattern.

In this study, we consider a bias reduction of the conditional maximum likelihood estimators for the unknown parameters of a Gaussian second-order moving average (MA(2)) model. In many cases, we use the maximum likelihood estimator because the estimator is consistent. However, when the sample size n is small, the error is large because it has a bias of $O({n^{-1}})$. Furthermore, the exact form of the maximum likelihood estimator for moving average models is slightly complicated even for Gaussian models. We sometimes rely on simpler maximum likelihood estimation methods. As one of the methods, we focus on the conditional maximum likelihood estimator and examine the bias of the conditional maximum likelihood estimator for a Gaussian MA(2) model. Moreover, we propose new estimators for the unknown parameters of the Gaussian MA(2) model based on the bias of the conditional maximum likelihood estimators. By performing simulations, we investigate properties of this bias, as well as the asymptotic variance of the conditional maximum likelihood estimators for the unknown parameters. Finally, we confirm the validity of the new estimators through this simulation study.

The air pollution caused by particulate matter (PM) has become a public health issue due to the risks to human life and the environment. The PM concentration in the air causes haze and affects the lungs and the heart, leading to reduced visibility, allergic reactions, pneumonia, asthma, cardiopulmonary diseases, lung cancer, and even death. In this context, the development of systems for monitoring, forecasting, and controlling emissions plays an important role. The literature about forecasting systems based on Artificial Neural Networks (ANNs) ensembles has been highlighted regarding statistical accuracy and efficiency. In this article, trainable and non-trainable combination methods are used for PM10 and PM2.5 (particles with an aerodynamic diameter less than 10 and 2.5 micrometers, respectively) time series forecasting for eight different locations, in Finland and Brazil, for different periods. Trainable ensembles based on ANNs, linear regression, and Copulas are compared with non-trainable combinations (mean and median), single ANNs, and linear statistical approaches. Different models are considered so far, including Autoregressive model (AR), Autoregressive and Moving Average Model (ARMA), Infinite Impulse Response Filters (IIR), Multilayer Perceptron (MLP), Radial Basis Function Networks (RBF), Extreme Learning Machines (ELM), Echo State Networks (ESN), and Adaptive Network Fuzzy Inference System (ANFIS). The use of ANNs ensembles, mainly combined with MLP, leads to a better one step ahead forecasting performance. The use of robust air pollution forecasting tools is prime to assist governments in managing air pollution issues like hospital collapse during adverse air quality situations. In this sense, our study is indirectly related to the following United Nations sustainable development goals: SDG 3 - good health and well-being and SDG 11 - sustainable cities and communities.

Background The novel coronavirus disease (COVID-19) has been recognized as a global threat, and several studies are being conducted using various mathematical models to predict the probable evolution of this epidemic, which are subject to potential bias. In this study, we aimed to assess and compare the impact of lockdown among the Punjab, Delhi, and Gujarat states of India using the Auto Regressive Integrated Moving Average (ARIMA) model by comparing forecasted COVID-19 data with real-time data. Methods We analyzed the COVID-19 data of Indian states from the index case until May 17, 2020. Auto Regressive Integrated Moving Average (1,1,3) (0,0,0) model was used to forecast the possible cumulative cases until May 17, from data up to May 3, and compared with real-time data. Recovery rate, case-fatality rate, and test per millions of states were collated. Results The trend of cumulative cases in Punjab was moving downward below the forecasted lower confidence limit (R-2 = 0.9799), whereas the cumulative case trend of Delhi was moving along the forecasted upper confidence limit with the forecasted data until May 3 (R-2 = 0.9971) and the trend of cumulative cases was below the forecasted upper confidence limit (R-2 = 0.9992) in Gujarat. Conclusions In Gujarat and Delhi, the lockdown was not effective in controlling the rise in COVID-19 cases even after the 56th day of lockdown, whereas the Punjab state succeeded in preventing havoc of COVID-19. In lieu of lockdown, using facemasks and improving ventilation in closed workspace settings, crowded spaces, and close-contact settings are more pragmatic than keeping away from others in India.

We propose a stochastic model for claims reserving that captures dependence along development years within a single triangle. This dependence is based on a gamma process with a moving average form of order $p \ge 0$ which is achieved through the use of poisson latent variables. We carry out Bayesian inference on model parameters and borrow strength across several triangles, coming from different lines of businesses or companies, through the use of hierarchical priors. We carry out a simulation study as well as a real data analysis. Results show that reserve estimates, for the real data set studied, are more accurate with our gamma dependence model as compared to the benchmark over-dispersed poisson that assumes independence.

Moving average (MA) is a time series model often used for pattern forecasting and recognition. It contains a noise that is often assumed to have a Gaussian distribution. However, in various applications, noise often does not have this distribution. This paper suggests using Laplacian noise in the MA model, instead. The comparison of Gaussian and Laplacian noises was also investigated to ascertain the right noise for the model. Moreover, the Bayesian method was used to estimate the parameters, such as the order and coefficient of the model, as well as noise variance. The posterior distribution has a complex form because the parameters are concerened with the combination of spaces of different dimensions. Therefore, to overcome this problem, the Markov Chain Monte Carlo (MCMC) reversible jump algorithm is adopted. A simulation study was conducted to evaluate its performance. After it has worked properly, it was applied to model human heart rate data. The results showed that the MCMC algorithm can estimate the parameters of the MA model. This was developed using Laplace distributed noise. Moreover, when compared with the Gaussian, the Laplacian noise resulted in a higher order model and produced a smaller variance.

Accurate ship trajectory plays an important role for maritime traffic control and management, and ship trajectory prediction with Automatic Identification System (AIS) data has attracted considerable research attentions in maritime traffic community. The raw AIS data may be contaminated by noises, which limits its usage in maritime traffic management applications in real world. To address the issue, we proposed an ensemble ship trajectory reconstruction framework combining data quality control procedure and prediction module. More specifically, the proposed framework implemented the data quality control procedure in three steps: trajectory separation, data denoising, and normalization. In greater detail, the data quality control procedure firstly identified outliers from the raw ship AIS data sample, which were further cleansed with the moving average model. Then, the denoised data were normalized into evenly distributed data series (in terms of time interval). After that, the proposed framework predicted ship trajectory with the artificial neural network. We verified the proposed model performance with two ship trajectories downloaded from public accessible AIS data base.

Traffic flow prediction is regarded as an important concept used in traffic planning, traffic design, and traffic management. In this study, the authors propose a multi-component attention (MCA) method for traffic flow prediction, which may jointly and adaptively understand components of long-term trends, seasons, and traffic flow residuals that result from multi-dimensional decomposition. According to the highly non-linear nature of traffic flow, the proposed module consists of a one-dimensional convolutional neural network, a bidirectional long short-term memory, and a bidirectional mechanism with an attention mechanism. The former captures local trend characteristics of residuals, while the latter captures trends and seasonal time adjustments. Due to the randomness, irregularity, and periodicity of traffic flow at intersections, target flow prediction is related to various sequences. Through the introduction of the attention mechanism, highly related historical information may be connected for multi-component flow data in the final prediction. Compared to seasonal autoregressive integral moving average model, artificial neural network, and recurrent neural network, the experimental results demonstrated that the proposed MCA model can meet the accuracy and effectiveness of complex non-linear urban traffic flow prediction models.

Forecasting macroeconomic and financial data are always difficult task to the researchers. Various statistical and econometrics techniques have been used to forecast these variables more accurately. Furthermore, in the presence of structural break,linear models are failed to model and forecast. Therefore, this study examines the forecasting performance of economic variables of G7 countries: France, Italy, Canada, Germany, Japan, UnitedKingdom and United States of America using non-linear autoregressive neural network (ARNN) model, linear auto regressive (AR) and Auto regressive integrated moving average model (ARIMA) models. The economic variables are inflation, exchange rate and Gross Domestic Product (GDP) growth for the period from 1970 to 2015. To measure the performance of the considered model Root,Mean Square Error, Mean Absolute Error and Mean Absolute Percentage Error are used. The results show that the forecasts from the non-linear neural network model are undoubtedly better as comparedto the AR and the Box–Jenkins ARIMA models

Wildfires are catastrophic disasters. They pose a fatal threat not only to the forest resources but also to the entire regime of flora and fauna, gravely disturbing the bio-diversity and ecology of the region. The frequency and severity of wildfires are expected to grow, owing to global warming. Therefore, it is essential to adopt a comprehensive, multifaceted approach that enables the real-time monitoring of forest terrains and prompt responsiveness. The Internet of Things (IoT) technology has grown exponentially in recent years, with IoT sensors being deployed to monitor and collect time critical data. This research proposes an integrated IoT-Fog-Cloud energy-efficient framework for wildfire prediction and forecasting. Initially, analysis of variance and Tukey’s post hoc test-based energy conserving mechanism ensures the enhanced lifetime of resource-constrained sensors by adapting the sampling rate of wildfire influent parameters (WIPs) at fog layer. Principal component analysis (PCA) is employed for WIPs’ reduction. Wildfire vulnerability level of a forest terrain is predicted and forecasted using Naive Bayes (NB) classifier and seasonal auto regressive integrated moving average model, respectively, at cloud layer. Burnt forest area is also predicted using support vector regression. The implementation results of the proposed framework prove its efficiency in predicting and forecasting wildfires.

Let {Yi;−∞

This note reconsiders the marginal density of a threshold moving average process and proposes a simple yet effective numerical algorithm to implement that by solving an associated integral equation. This algorithm can also be applied to calculate stationary probability density or distribution functions of a few other types of nonlinear stationary stochastic processes numerically.

Sediment accumulation in a river reservoir is studied by stochastic time series models and analytical approach. The first-order moving average process is found the best for the suspended sediment d...

Lahore and Dhaka are rapid expanding and over populated cities of South Asia located in Pakistan andBangladesh respectively. The present study focuses on the evaluation of temperature variability in comparison of bothcities. This study primarily aims at the assessment and examination of temperature variations in both mega cities ofSouth Asia which are seasonal as well as the annual. The time series data were analysed by using statistical techniquesAutoregressive Moving Average Model (ARMA) and Autoregressive Integrated Average Model (ARIMA). The resultsreveal that the minimum temperature is increasing much faster than that of the maximum temperature of both cities.However, the temperature rise(in maximum and minimum) has been observed highest during the spring seasons in bothcities.

This study aims to analyze and select the most accurate forecasting for predicting cassava prices in Indonesia. The data used is monthly data during the period of 2009 to 2017. This predicting uses the forecasting model, such as Moving Average, Exponential Smoothing, and Decomposition. Selecting the models found by comparing the smallest values of MAPE, MAD, and MSD. Therefore, it concluded that the Moving Average model is the most appropriate to Forecasting the price of cassava. Keywords : Selection, Forecasting model, cassava, prices

An uncertain time series (UTS) is a sequence of uncertain observed values taken sequentially in time. As a basic UTS model, an uncertain autoregressive (UAR) model has been investigated. This paper...

In this study, a new Multivariable Grey Model (1,m) aimed at interval grey number sequences with known possibility functions is built using the kernel and degree of greyness under new definitions. Based on the new model, formulae are deduced to calculate and predict the upper and lower bounds of interval grey numbers. Since the grey system model and fog- and haze-prone weather have the same characteristics of uncertainty, this model was applied to simulate and predict the measurable indicators of fog and haze in Nanjing, China. We selected visibility data and particulate matter data with a diameter of 2.5 µm to build a new Multivariable Grey Model (1,2) with a new kernel and degree of greyness sequence. In addition, we established the traditional Multivariable Grey Model (1,2) with the original kernel and degree of greyness and the Auto-Regressive Integrated Moving Average Model (1,1,0). The results show that the new Multivariable Grey Model (1,2) has the best simulation and prediction effects among the three models, with average relative errors of simulation and prediction at 1.32% and 0.32%, respectively. To further verify the validity and feasibility of the proposed model, we added another real-world example to establish the three models mentioned above. The results prove that the proposed model has evidently superior performance to another two models.

Online portfolio selection and simulation are some of the most important problems in several research communities, including finance, engineering, statistics, artificial intelligence, machine learning, etc. The primary aim of online portfolio selection is to determine portfolio weights in every investment period (i.e., daily, weekly, monthly, etc.) to maximize the investor’s final wealth after the end of investment period (e.g., 1 year or longer). In this paper, we present an efficient online portfolio selection strategy that makes use of market indices and benchmark indices to take advantage of the mean reversal phenomena at minimal risks. Based on empirical studies conducted on recent historical datasets for the period 2000 to 2015 on four different stock markets (i.e., NYSE, S&P500, DJIA, and TSX), the proposed strategy has been shown to outperform both Anticor and OLMAR — the two most prominent portfolio selection strategies in contemporary literature.

Funding Information: Funding: The research was funded by National Natural Science Foundation of China (grant num‐ ber: 72071041), Transportation Science and Technology Demonstration Project of Jiangsu Province, (grant number: 2018Y02), and China Society of Logistic (grant number: 2021CSLKT3‐096). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. The purpose of this study is to investigate whether spatial-temporal dependence models can improve the prediction performance of short-term freight volume forecasts in inland ports. To evaluate the effectiveness of spatial-temporal dependence forecasting, the basic time series forecasting models for use in our comparison were first built based on an autoregression integrated moving average model (ARIMA), a back-propagation neural network (BPNN), and support vector regression (SVR). Subsequently, combining a gradient boosting decision tree (GBDT) with SVR, an SVR- GBDT model for spatial-temporal dependence forecast was constructed. The SVR model was only used to build a spatial-temporal dependence forecasting model, which does not distinguish spatial and temporal information but instead takes them as data features. Taking inland ports in the Yangtze River as an example, the results indicated that the ports’ weekly freight volumes had a higher autocorrelation with the previous 1–3 weeks, and the Pearson correlation values of the ports’ weekly cargo volume were mainly located in the interval (0.2–0.5). In addition, the weekly freight volumes of the inland ports were higher depending on their past data, and the spatial-temporal dependence model improved the performance of the weekly freight volume forecasts for the inland river. This study may help to (1) reveal the significance of spatial correlation factors in ports’ short-term freight volume predictions, (2) develop prediction models for inland ports, and (3) improve the planning and operation of port entities.

COVID-19 was declared a global pandemic by the World Health Organization in March 2020, and has infected more than 4 million people worldwide with over 300,000 deaths by early May 2020. Many researchers around the world incorporated various prediction techniques such as Susceptible–Infected–Recovered model, Susceptible–Exposed–Infected–Recovered model, and Auto Regressive Integrated Moving Average model (ARIMA) to forecast the spread of this pandemic. The ARIMA technique was not heavily used in forecasting COVID-19 by researchers due to the claim that it is not suitable for use in complex and dynamic contexts. The aim of this study is to test how accurate the ARIMA best-fit model predictions were with the actual values reported after the entire time of the prediction had elapsed. We investigate and validate the accuracy of an ARIMA model over a relatively long period of time using Kuwait as a case study. We started by optimizing the parameters of our model to find a best-fit through examining auto-correlation function and partial auto correlation function charts, as well as different accuracy measures. We then used the best-fit model to forecast confirmed and recovered cases of COVID-19 throughout the different phases of Kuwait’s gradual preventive plan. The results show that despite the dynamic nature of the disease and constant revisions made by the Kuwaiti government, the actual values for most of the time period observed were well within bounds of our selected ARIMA model prediction at 95% confidence interval. Pearson’s correlation coefficient for the forecast points with the actual recorded data was found to be 0.996. This indicates that the two sets are highly correlated. The accuracy of the prediction provided by our ARIMA model is both appropriate and satisfactory.

In view of the large hysteresis in the measurement of the inlet NOx concentration,simply using single-step prediction method cannot solve this problem well. In order to better solve the problem of measurement lag and improve control quality, a multi-step prediction model based on variational modal decomposition-autoregressive differential moving average model (VMD-ARIMA) is proposed, which is convenient for the system to control the amount of ammonia injection online and reduce NOx emissions. Firstly, the appropriate number of decomposition levels for the inlet NOx time series is selected, and then VMD decomposition is performed. Secondly, use the order criterion to determine the order of each layer component, and then use the ARIMA model to make multi-step predictions, and keep the model updated online. Finally, the multi-step prediction values of each layer are superimposed correspondingly to obtain the final multi-step prediction value sequence. The results show that: First, compared to the Back Propagation Neural Network(BP) model and Least Square Support Vector Machine(LSSVM) model, the VMD-ARIMA model has simpler inputs and can predict the inlet NOx concentration more accurately. Second, compared with the ARIMA model, the VMD-ARIMA model has better multi-step prediction performance, higher prediction accuracy, and can predict the amount of NOx generated one minute in advance.

In order to improve the reliability and accuracy of mine gas concentration prediction, a prediction model based on wavelet noise reduction and autoregressive differential moving average model (ARIMA) is proposed. the original data is decomposed, thresholded and reconstructed, and the noise in the time series data is stripped, and then the ARIMA module of Python is called to build a prediction model to fit the prediction data, The ARIMA (2,1,1) model parameters were selected to fit the best prediction model, and the prediction effect was tested. Research shows that the method based on wavelet noise reduction and ARIMA prediction model can effectively improve the prediction accuracy and reliability of gas concentration prediction in the short-term. The prediction results of this algorithm are compared with other prediction models. The prediction model can not only reflect the change trend of gas emission concentration, but also has high fitting effect and prediction accuracy.

In this article, a first order random coefficient autoregressive model with Laplace distribution as marginal is developed. A random coefficient moving average model of order one with Laplace as marginal distribution is introduced and its properties are studied. By combining the two models, a first order random coefficient autoregressive moving average model with Laplace marginal is developed and discussed its properties. Various theorems based on the new developed models are shown. The simulated sample path is generated from first order autoregressive Laplace process from a set of observations. A first order random coefficient moving average process with generalized Laplace innovations is also obtained.

Macroeconomists increasingly use external sources of exogenous variation for causal inference. However, unless such external instruments (proxies) capture the underlying shock without measurement error, existing methods are silent on the importance of that shock for macroeconomic fluctuations. We show that, in a general moving average model with external instruments, variance decompositions for the instrumented shock are interval-identified, with informative bounds. Various additional restrictions guarantee point identification of both variance and historical decompositions. Unlike SVAR analysis, our methods do not require invertibility. Applied to U.S. data, they give a tight upper bound on the importance of monetary shocks for inflation dynamics.

This paper investigates the behavior of daily exchange rate of the Georgian Currency LARI (GEL) exchange rate against the USDand EUR. To forecast exchange rates there are numerous models, which tend from very simple to very complicated models for analysis of GEL/USD and GEL/EUR time series variable. The objective of this paper is to com- pare the performance of individual time series models for predictingexchange rates. We will investigate the application of following time series analysis models: moving average, ex- ponential smoothing, double exponential smoothing adjust- ed for trend, time-series decomposition models, and ARIMA class models. The forecasting ability of these models is subsequently assessed using the symmetric loss functions which are the Mean Absolute Percentage Error (MAPE), the Mean Absolute deviation (MAD), and the Mean Squared error /deviation (MSE/MSD). In some cases, predicting the direction of exchange rate change may be valuable and profitable. Hence, it is reasonable to look at the frequency of the correctpredicted direction of change by used models, for short - FCPCD. An exchange rate represents the price of one currency in terms of another. It reflects the ratio at which one currency can be exchanged with another currency. Exchange rates forecasting is a very important and challenging subject of finance market, to determine optimal government policies as well as to make business decisions. This is important for all that firms which having their business spread over different countries or for that which raise funds in different currency. Business people mainly use exchange rates forecasting results in following types of decisions like choice currency for invoicing, pricing transactions, borrowing and landing currency choice, and management of open currency positions. The forex market is made up of banks, commercial companies, central banks, investment management firms, hedge funds, and retail forex brokers and investors. Forecasting the short- run fluctuations and direction of change of the currency ex- change rates is important for all these participates. The main goal of this study is to forecast of future ex- change rate trends by using currency rates time-series, rep- resenting past trends, patterns and waves. The monetary policy of the National Bank of Georgia since 2009 have been followed the inflation targeting regime, where exchange rate regime is floating - change of exchange rate is free. The offi- cial exchange rate of the Georgian GEL against the USD is cal- culated each business day. The official exchange rate of GEL against USD is calculated as the average weighted exchange rate of the registered spot trades on the interbank market functioning within the Bloomberg trade platform. Then, the official exchange rate of GEL against other foreign currencies is determined according to the rate on international markets on the basis of cross-currency exchange rates.

We consider a L\'evy driven continuous time moving average process $X$ sampled at random times which follow a renewal structure independent of $X$. Asymptotic normality of the sample mean, the sample autocovariance, and the sample autocorrelation is established under certain conditions on the kernel and the random times. We compare our results to a classical non-random equidistant sampling method and give an application to parameter estimation of the L\'evy driven Ornstein-Uhlenbeck process., Comment: 27 pages, 4 figures

The main focus of this paper is topological crackle, the layered structure of annuli formed by heavy-tailed random points in R d . In view of extreme value theory, we study the topological crackle generated by a heavy-tailed discrete-time moving average process. Because of the clustering effect of a moving average process, various topological cycles are produced consecutively in time in the layers of the crackle. We establish the limit theorems for the Betti numbers, a basic quantifier of topological cycles. The Betti number converges to the sum of stochastic integrals, some of which induce multiple cycles because of the clustering effect.

Purpose The purpose of this study is to select the appropriate forecasting model at the retail stage for selected vegetables on the basis of performance analysis. Design/methodology/approach Various forecasting models such as the Box–Jenkins-based auto-regressive integrated moving average model and machine learning-based algorithms such as long short-term memory (LSTM) networks, support vector regression (SVR), random forest regression, gradient boosting regression (GBR) and extreme GBR (XGBoost/XGBR) were proposed and applied (i.e. modeling, training, testing and predicting) at the retail stage for selected vegetables to forecast demand. The performance analysis (i.e. forecasting error analysis) was carried out to select the appropriate forecasting model at the retail stage for selected vegetables. Findings From the obtained results for a case environment, it was observed that the machine learning algorithms, namely LSTM and SVR, produced the better results in comparison with other different demand forecasting models. Research limitations/implications The results obtained from the case environment cannot be generalized. However, it may be used for forecasting of different agriculture produces at the retail stage, capturing their demand environment. Practical implications The implementation of LSTM and SVR for the case situation at the retail stage will reduce the forecast error, daily retail inventory and fresh produce wastage and will increase the daily revenue. Originality/value The demand forecasting model selection for agriculture produce at the retail stage on the basis of performance analysis is a unique study where both traditional and non-traditional models were analyzed and compared.

In modern microprocessor and SoC designs, supply noise margin has been significantly reduced due to the continuously decreasing supply voltage level. On the other hand, with increasing current density, chips may see larger supply noise variations on various spots and from time to time. As a result, chip robustness and reliability are inevitably deteriorated with more frequent supply noise emergencies. It is therefore crucial to have an efficient supply noise prediction method to enhance design robustness. The state-of-art solutions either try to build a spatial noise estimation framework at the layout-level using the limited distributed physical noise sensors or attempt to develop emergency predictors at the architecture-level thus ignore back-end power delivery details. In this paper, we propose a cross-layer framework for temporal supply noise prediction. Our method not only accounts for the temporal characteristics of workload execution at micro-architecture-level but also incorporates the power delivery model at the circuit-level into such system-level prediction. In order to enable the capability of on-the-fly noise prediction, we first bridge the gap between system-level workload and micro-architectural-level power by employing an ordinary least square-based power estimation model and an adaptive auto-regressive integrated moving average model (ARIMA)-based power prediction model. Then a layout-level supply noise model is developed to explore the correlations between micro-architectural-level power and layout-level supply noise. Compared with existing methods, the proposed ARIMA-based power model improves the prediction performance by up to 37.5%/63.0% in X86/ARM. Moreover, compared with SPICE simulation, our framework is able to estimate present supply noise with an average error of 0.005% and predict future supply noise with an average error of 1.58%/1.17% for X86/ARM architecture.

The main aim of this paper is to model the industrial power consumption in Nigeria with the Adaptive Neuro-Fuzzy Inference System (ANFIS) model and then forecast the industrial power consumed for the next five years beyond the available data. About 45 years (1970 to 2015) dataset was obtained from the Central Bank of Nigeria (CBN), the National Bureau of Statistics (NBS) and other relevant organizations. The data includes population, rainfall, electricity connectivity and temperature which are the explanatory variables. Matlab was used along with the dataset to train and evaluate the ANFIS model which was then used to forecast the industrial power consumption in Nigeria for the years 2016 to 2020.The prediction performance of the ANFIS model was compared to those of Autoregressive Moving Average model and Moving Average model. From the result obtained, ANFIS gave R-square value of 0.9977 (99.77%), SSE value of 395.3674 and RMSE value of 2.9641. The regression coefficient of 99.77% shows that about 99.77% of the variations in the industrial power consumption in Nigeria for the years 1970 to 2015 are explained by the selected explanatory variables. The forecast result showed that the Nigerian industrial power consumption would be about 374.7 MW at the end of 2020 which is about 73.1% increase from the industrial power consumption in 2015. As such, based on the industrial power consumption in 2015, over 73% increment in power supply to the industrial sector will be required to satisfy the industrial sector's power demand in 2020.

In this work, we propose the inclusion of the available information of measured or estimated disturbances in the modifier adaptation methodology for real-time optimization (RTO). The idea is to extend the applicability of this technique to processes wherein the disturbances affect the quantities involved in the necessary optimality conditions of the process. To do so, we include the estimation of process gradients with respect to both the decision variables and disturbances in the methodology. This approach was performed in a laboratory-scale flotation column, where the effects of changes in the feed characteristics on the economic performance of the process were analyzed. The influence of the availability of the disturbance information was also analyzed, considering immediate and delayed availability. In the latter case, the auto regressive integrated moving average model (ARIMA) was used as an estimator in each RTO iteration. The results show that the inclusion of the available disturbance information enables tracking of the optimum of the process under continuously changing feed conditions.

Many developing countries, Malaysia included, are constantly faced with problems in managing water resources as there is lack of integration and holistic approach with little participation from the general public and other stakeholders apart from the government. In this study, two quantitative models, which are the discrete dynamical system and moving average, is applied to obtain the forecast value of clean water in Malaysia’s river basin by using open source data with minimal cost of analysis. The findings suggested that moving average method is superior as it provides better accuracy in forecasting with small error rate. The method is easy to understand, used standard MS Excel in computing, and need only minimal requirement of the machine’s operating system. Continuous assessment to the quality level of clean water in Malaysia’s river basin should be strictly regulated to ensure the right course of action to manoeuvre effective countermeasure for this issue. Among the counter measures may be in a form of focused education towards specified target groups, regulatory exercises, as well as awareness campaigns that are more effectively arranged.

This study focuses on a fuzzy generalised predictive control (FGPC) method for a time-delay hydro-turbine governing system (HTGS). First, based on the time-delay Takagi-Sugeno fuzzy model, a time-delay HTGS and its fuzzy prediction model are given. Second, with the help of delay fuzzy linearisation and a fourth-order Runge-Kutta algorithm, a transformed controlled auto-regressive integrated moving average model is obtained. Then, a new FGPC scheme for the time-delay HTGS is proposed. Finally, numerical simulations are implemented to verify the validity and superiority of the proposed method. It also provides a reference for the stability control of relevant hydropower station systems.

Pennsylvania and Texas accounted for about 60% of U.S. total shale gas production. Better forecasting shale gas production in Pennsylvania and Texas can serve us to better predict U.S. shale gas production. In this work, we integrate the linear and nonlinear forecasting techniques in order to use the advantages and avoid the disadvantages of linear and nonlinear forecasting models, so as to improve forecasting accuracy. Specifically, we develop two hybrid forecasting techniques, i.e., nonlinear metabolic grey model–Autoregressive Integrated Moving Average Model (NMGM-ARIMA), and Autoregressive Integrated Moving Average Model - Artificial neural network (ARIMA-ANN). 60 samples (monthly shale gas production in Pennsylvania and Texas) are used to test these two proposed forecasting techniques and these existing single nonlinear (NMGM, and ANN) and linear (ARIMA) forecasting techniques. The results show that for samples from either Pennsylvania or Texas, the mean absolute percent error of NMGM-ARIMA (3.16%, 1.64%) is smaller than that of NMGM (4.31%, 2.98%) and ARIMA (3.53%, 2.03%), and that of ARIMA-ANN (2.06%, 1.38%) is also smaller than ARIMA (3.53%, 2.03%) and ANN (3.09%, 1.71%). The proposed hybrid NMGM-ARIMA and ARIMA-ANN can achieve more accurate forecasting effect than the single theory-based models that made them up, and can be used in forecasting other fuels. The forecasting results show growth rates of shale gas production in Pennsylvania is higher than Texas in 2017 and 2018.

In this paper, we study the discrete-time additive Gaussian noise channel given by $$Y_i=X_i+Z_i$$ , $$i=1,2,\ldots ,$$ where the input signal $$\{X_i\}$$ satisfies an average power constraint and the noise $$\{Z_i\}$$ is a stationary Gaussian process. We are interested in the capacity of the channel with feedback. Despite numerous lower and upper bounds having been reported, except for some special cases, it is an open problem to find the closed form for the feedback capacity. In the paper, we consider the case where the Gaussian noise $$\{Z_i\}$$ is a first-order moving average process defined by $$Z_i=W_i+\alpha W_{i-1}$$ , $$|\alpha |\le 1$$ , with white Gaussian innovations $$W_i$$ , $$i=0,1,2,\ldots$$ . For this channel, Kim (IEEE Trans Inf Theory 52(7):3063–3079, 2006; IEEE Trans Inf Theory 56(1):57–85, 2010) showed that a modified Schalkwijk–Kailath scheme achieves the feedback capacity and obtained the closed form of the feedback capacity. The main aim of the paper is to give a new proof of the optimality of the coding scheme given by Kim. In our proof, decompositions of the input signal $$X_i$$ and the Gaussian innovation $$W_i$$ into independent components play important roles. For this channel, the minimum decoding error probability decreases with an exponential order which linearly increases with block length.

Background/Objective: To provide an efficient predictive technique to foresee future workload as well as to handle the resources efficiently by performing hybrid auto scaling for Cloud applications. Cloud applications might expertise completely different workload at different times, automatic provisioning has to work with efficiency at any point of time. Auto scaling is a feature of cloud computing that potentially scale the resources in line on demand. Considering this expectation, they are generally categorized into Reactive scaling which adds or reduces resources based on a fixed threshold value. The predictive scaling is used provide necessary scaling actions beforehand. Methods/Statistical Analysis: To perform the hybrid auto scaling (reactive plus predictive auto scaling), a time series technique should be used. Auto-regressive Moving Average (ARMA) model, the Exponential Smoothing (ES) model, the Autoregressive model (AR), the Moving Average model (MA) and the Trend- Adjusted Exponential Smoothing (TAES), Auto Regressive Integrated Moving Average (ARIMA) Time-series model, Naive bayes algorithm, Recurrent Neural Network- Long Short Term Memory (RNN-LSTM), Independent Recurrent Neural Network (IndRNN) are time series techniques used to foresee the future workload. To find the effectiveness of predictive techniques, Mean Absolute Error (MAE), Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) performance metrics are evaluated. Findings: Based on the evaluation, IndRNN gives the minimum error rate. IndRNN is used to predict the future resource requisites in order to ascertain adequate resource are available ahead of time. Application: The predicted result from IndRNN method is integrated on private cloud to autoscale the resources for cloud applications. Keywords: Cloud Applications, Hybrid Autoscaling, Independent Recurrent Neural Network (IndRNN), Private Cloud, Workload

With a view to providing a tool to accurately model time series processes which may be corrupted with errors such as measurement, round-off and data aggregation, this study developed an integrated moving average (IMA) model with a transition matrix for the errors resulting in a convex combination of two ARMA errors. Datasets on interest rates in the United States and Nigeria were used to demonstrate the application of the formulated model. Basic tools such as the autocovariance function, maximum likelihood method, Newton–Raphson iterative method and Kolmogorov–Smirnov test statistic were employed to examine and fit the formulated specification to data. Test results showed that the proposed model provided a generalisation and a more flexible specification than the existing models of AR error and ARMA error in fitting time series processes in the presence of errors.

From the principles of statistical process control, the observations are assumed to be identically and independently normally distributed, although this assumption is frequently untrue in practice. Therefore, control charts have been developed for monitoring and detecting data which are autocorrelated. Recently, a modified exponentially weighted moving average (EWMA) control chart has been introduced that is a correction of the EWMA statistic and is very effective for detecting small and abrupt changes in independent normally distributed or autocorrelated observations. In this study, the performance of a modified EWMA chart is investigated by examining the 2 sides of the exact average run length based on an explicit formula when the observations are from a general-order moving average process with exponential white noise. A performance comparison of the EWMA and the modified EWMA control charts is also presented. In addition, the performance of the modified and EWMA control charts is contrasted using Dow Jones composite average from a real-life dataset. The findings suggest that the modified EWMA control chart is more sensitive than the EWMA control chart for almost every case of the studied smoothing parameter and constant values of the control chart. HIGHLIGHTS Autocorrelation data is frequency untrue of assumption practice in time series data Modified EWMA is a new control chart that is effective for detecting change in independent normal distribution and autocorrelated observations The efficiency of the control chart is measured by average run length Explicit formula is easy to derive and provides the exact value of the average run length

Turkey attaches particular importance to energy generation by renewable energy sources in order to remove negative economic, environmental and social effects caused by fossil resources in energy generation. Renewable energy sources are domestic and do not have any negative effect, such as external dependence in energy and greenhouse gas, caused by fossil resources and which constitute a threat for sustainable economic development. In this respect, the prediction of energy amount to be generated by Renewable Energy (RES) is highly important for Turkey. In this study, a generation forecasting was carried out by Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Average (ARIMA) methods by utilising the renewable energy generation data between 1965-2019. While it was predicted by ANN that 127.516 TWh energy would be generated in 2023, this amount was estimated to be 45.457 TeraWatt Hour (TWh) by ARIMA (1.1.6) model. The Mean Absolute Percentage Error (MAPE) was calculated in order to specify the error margin of the forecasting models. This value was determined to be 13.1% by ANN model and 21.9% by ARIMA model. These results suggested that the ANN model provided a more accurate result. It is considered that the conclusions achieved in this study will be useful in energy planning and management.