Search

This study introduced the optimized block bootstrap (OBB), a novel method designed to enhance time series prediction by reducing the number of blocks while maintaining their representativeness. OBB minimized block overlap, resulting in greater computational efficiency while preserving the temporal structure of data. The method was evaluated through extensive simulations of autoregressive moving average (ARMA) models and South Africa economic data which included inflation rates, gross domestic product (GDP) growth, interest rates, and unemployment rates. Results demonstrated that OBB consistently outperformd circular block bootstrap (CBB), providing more accurate forecasts with lower root mean square error (RMSE), which assessed variance, and lower mean absolute error (MAE), which measured bias, across various time series models and parameter settings. Consequently, the OBB method was applied to forecasting of the South Africa economic data, extending up to 2027. The novel approach presented by OBB offered a valuable tool for improving predictive accuracy in time series forecasting, with potential applications across diverse fields such as finance and environmental modeling.

This study introduced the optimized block bootstrap (OBB), a novel method designed to enhance time series prediction by reducing the number of blocks while maintaining their representativeness. OBB minimized block overlap, resulting in greater computational efficiency while preserving the temporal structure of data. The method was evaluated through extensive simulations of autoregressive moving average (ARMA) models and South Africa economic data which included inflation rates, gross domestic product (GDP) growth, interest rates, and unemployment rates. Results demonstrated that OBB consistently outperformd circular block bootstrap (CBB), providing more accurate forecasts with lower root mean square error (RMSE), which assessed variance, and lower mean absolute error (MAE), which measured bias, across various time series models and parameter settings. Consequently, the OBB method was applied to forecasting of the South Africa economic data, extending up to 2027. The novel approach presented by OBB offered a valuable tool for improving predictive accuracy in time series forecasting, with potential applications across diverse fields such as finance and environmental modeling. [ABSTRACT FROM AUTHOR]

The accurate prediction of hourly global solar radiation is critical to solar energy conversion systems selecting appropriate provinces, and even future investment policies. With this viewpoint, the aim of this study is to predict one hour ahead the global solar radiation of six provinces that have different climate regions in Turkey. A deep learning technique based on the LSTM neural network is used for this purpose. To see the success of this proposed model, the deep learning technique GRU, a machine-learning approach ANFIS with FCM, and standard statistical models ARMA, ARIMA, and, SARIMA are also applied. Forecasting models developed to estimate the future values of hourly global solar radiation are based on past time series data. The study discusses four different statistical metrics (MAE, RMSE, NSE, and, R) to determine the success of these algorithms. The results indicate that the R, MAE, NSE, and, RMSE values of the two approaches range from 0.9352 to 0.9806, from 29.0737 to 59.4840 Wh/m2, from 0.6445 to 0.9686 and from 57.4492 to 94.3287 Wh/m2, respectively. The results revealed that the methods used in the study performed satisfactorily in terms of hourly solar radiation estimation, but the LSTM method performed better. [ABSTRACT FROM AUTHOR]

The amount of annual rainfall in the Bengawan Solo watershed causes high water flow (water discharge) in several rivers. In addition, high flow rates significantly increased the water surface level at some observation sites. The Bengawan Solo River burst its banks in November 2016, causing flooding in several areas in eastern Solo. At that time, the river stage at the Jurug monitoring post passed ten. Therefore, a flood early warning system would be useful for predicting water levels in this context. Every day, one post on the Bengawan Solo River measures the water level. The time series data used in this study is the water level. Autoregressive Moving Average (ARMA) is a predictive method for measuring time set data. The assumption of homoscedasticity or constant error variance is used in this model. However, the study will use the ARMA model if the variance changes randomly. The study used 60 pieces of data from January to February 2018. This study can directly use ARMA because the data results are stationary based on ADF value 0.0036. After the first pause, the ACF and PACF are disconnected based on the correlogram pattern. This shows that the water level of the Bengawan Solo River in that period can appear on the Autoregressive Moving Average with orders p = 1 and q = 1 ARMA(1,1). Thus, the total average residue is 0.0668384, so the short error is 6.68384%.

Bootstrap method was initially used to determine accuracy measures for sample estimates of independent and identical distributions (i.i.d.). In order to apply bootstrap method to time-dependent data, blocking technique is introduced to preserve serial correlation of the original time series data. In the past, resampling techniques for time-dependent data were implemented using Non-overlapping Block Bootstrap (NBB) method but its dichotomous block arrangement restricts the number of blocks. As a result, improvement becomes necessary. Although the Moving Block Bootstrap (MBB) method improves upon NBB with regard to many more blocks, it introduces an uneven representation of the time series elements which eventually influences its accuracy. In this paper, an innovative method called Moving Block Bootstrap Method with better element Representation (MBBR) is developed to ensure that the time series elements within the block are better represented with minimum number of elements. To compare MBB and MBBR, simulated studies were carried out on some set time series data following each classes of Autoregressive Moving Average (ARMA) model with different parameters, sample sizes and standard deviation using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Results show that by improving the representation of time series data in the blocking arrangement, the accuracy of the proposed method (MBBR) consistently outperforms the existing one (MBB) and thus, provides more efficient estimates of the dependent variable. [ABSTRACT FROM AUTHOR]

Wind speed prediction is very important in the field of wind power generation technology. It is helpful for increasing the quantity and quality of generated wind power from wind farms. By using univariate wind speed time series, this paper proposes a hybrid wind speed prediction model based on Autoregressive Moving Average-Support Vector Regression (ARMA-SVR) and error compensation. First, to explore the balance between the computation cost and the sufficiency of the input features, the characteristics of ARMA are employed to determine the number of historical wind speeds for the prediction model. According to the selected number of input features, the original data are divided into multiple groups that can be used to train the SVR-based wind speed prediction model. Furthermore, in order to compensate for the time lag introduced by the frequent and sharp fluctuations in natural wind speed, a novel Extreme Learning Machine (ELM)-based error correction technique is developed to decrease the deviations between the predicted wind speed and its real values. By this means, more accurate wind speed prediction results can be obtained. Finally, verification studies are conducted by using real data collected from actual wind farms. Comparison results demonstrate that the proposed method can achieve better prediction results than traditional approaches. [ABSTRACT FROM AUTHOR]

As one of the fastest-growing new energy sources, wind power technology has attracted widespread attention from all over the world. In order to improve the quality of wind power generation, wind speed prediction is an indispensable task. In this paper, an error correction-based Variational Mode Decomposition and Broad Learning System (VMD-BLS) hybrid model is proposed for wind speed prediction. First, the wind speed is decomposed into multiple components by the VMD algorithm, and then an ARMA model is established for each component to find the optimal number of sequence divisions. Second, the BLS model is used to predict each component, and the prediction results are summed to obtain the wind speed forecast value. However, in some traditional methods, there is always time lag, which will reduce the forecast accuracy. To deal with this, a novel error correction technique is developed by utilizing BLS. Through verification experiment with actual data, it proves that the proposed method can reduce the phenomenon of prediction lag, and can achieve higher prediction accuracy than traditional approaches, which shows our method's effectiveness in practice. [ABSTRACT FROM AUTHOR]

Fault prognosis based on single model is generally inaccurate due to the varying working conditions. A multistage fault prognosis methodology combining stage identification with Bayesian networks (BNs) and time series approach with particular emphasis on the autoregressive moving average (ARMA) model is proposed to solve this problem. In the first stage, degradation data are identified, and outliers are marked by the Euclidean distance. Degenerate attributes of outliers are finely identified by BNs and matched to the corresponding model. In the second stage, the ARMA model is used for prognosis according to the results of the fine identification. Subsequently, the double-precision identification and ARMA submodel prognosis are carried out alternately throughout the prognosis process. Three degradation types of permanent magnet synchronous motor are simulated to verify the applicability of the method. Result shows that it can track the changes in the degradation in time and obtains better results. [ABSTRACT FROM AUTHOR]

ABSTRAK Seorang investor pada umumnya berharap untuk membeli suatu saham dengan harga yang rendah dan menjual saham tersebut dengan harga yang lebih tinggi untuk memperoleh imbal hasil yang tinggi. Namun, kapan waktu yang tepat melakukannya menjadi tantangan tersendiri bagi para investor. Oleh sebab itu, dibutuhkan suatu model yang mampu menduga imbal hasil saham dengan baik, salah satunya adalah model autoregressive moving average (ARMA). Tujuan dari penelitian ini adalah untuk menerapkan model autoregressive (AR), model moving average (MA), atau model autoregressive moving average (ARMA) pada data observasi untuk menduga imbal hasil saham bank central asia (BCA). Terdapat empat prosedur dalam membangun sebuah model AR, MA atau ARMA. Pertama, data yang digunakan harus weakly stationary. Kedua, orde dari model harus diidentifikasi untuk memperoleh model yang terbaik. Ketiga, parameter setiap model harus ditentukan. Keempat, kelayakan model harus diperiksa dengan melakukan analisis residual untuk memperoleh model yang terbaik. Pada akhirnya, model ARMA (1,1) adalah model terbaik dan akurat dalam menduga imbal hasil saham BCA. ABSTRACT Generally, investor always wish to be able to buy a stock at a low price and sell it at a higher price to obtain high returns. However, when is the best time to buy or sell it is a challenge for investor. Therefore, proper models are needed to predict a stock return, one of them is autoregressive moving average (ARMA) model. The first purpose of this paper is to apply the autoregressive (AR), moving average (MA) or ARMA models to the observations to predict stock returns. There are four procedures which is used to build an AR, MA, or ARMA model. First, the observations must be weakly stationary. Second, the order of the models must be identified to obtain the best model. Third, the unknown parameters of the models are estimated by maximum likelihood. Fourth, through residual analysis, diagnostic checks are performed to determine the adequacy of the model. In this paper, stock returns of BCA are used as data observation. Finally, the ARMA (1,1) model is the best model and appropriate to predict the stock returns BCA in the future.

As one of the fastest-growing new energy sources, wind power technology has attracted widespread attention from all over the world. In order to improve the quality of wind power generation, wind speed prediction is an indispensable task. In this paper, an error correction-based Variational Mode Decomposition and Broad Learning System (VMD-BLS) hybrid model is proposed for wind speed prediction. First, the wind speed is decomposed into multiple components by the VMD algorithm, and then an ARMA model is established for each component to find the optimal number of sequence divisions. Second, the BLS model is used to predict each component, and the prediction results are summed to obtain the wind speed forecast value. However, in some traditional methods, there is always time lag, which will reduce the forecast accuracy. To deal with this, a novel error correction technique is developed by utilizing BLS. Through verification experiment with actual data, it proves that the proposed method can reduce the phenomenon of prediction lag, and can achieve higher prediction accuracy than traditional approaches, which shows our method’s effectiveness in practice.

Over the past few years, there is a vast expansion of the Jordan National Energy Sector. Hence, National Electrical Power Company (NEPCO) sheds more light on load forecasting. It tries to build a rigid bridge between the academic and industrial fields. Subsequently, this work presents a study of short-term load forecasting (STLF) for the Jordanian power system. Three techniques are used: the nonlinear autoregressive exogenous model (NARX) recurrent neural network, the Elman neural network, and the autoregressive moving average (ARMA). These proposed techniques are trained, validated, and tested using the historical record of hourly load data for the whole year 2018, which is obtained from NEPCO. Besides, these techniques show a satisfactory forecasting accuracy and improve the predicted load shape performance of a week ahead (January 1, 2019, to January 7, 2019). Error is reduced based on optimizing the number of hidden layers and the number of neurons. Moreover, the mean absolute percentage errors (MAPEs) are estimated at 5.53%, 3.42%, and 10.28% for NARX, Elman, and ARMA, respectively. Finally, this work is implemented using neural network toolbox and MATLAB code in Mathworks. [ABSTRACT FROM AUTHOR]

This paper proposes a fusion model based on the autoregressive moving average (ARMA) model and Elman neural network (NN) to achieve accurate prediction for the state of health (SOH) of lithium-ion batteries. First, the voltage and capacity degradation variation of the battery are acquired through the battery lifecycle data, and the health factor related to the battery aging is selected according to the variation of the voltage profile. Second, the empirical mode decomposition (EMD) is employed to process the capacity degradation data and eliminate the phenomenon of tiny capacity recovery, and multiple data sequences, as well as the related residue, are extracted, then the grey relational analysis (GRA) between sub-sequences and health factor are discussed. Furthermore, the ARMA model and Elman NN model are respectively built by training the subsequent time series data and residue data. Finally, all the individual predictions are combined to generate the estimated SOH sequences. The experimental validation is performed to manifest that the addressed fusion method performs the SOH prediction with satisfactory accuracy, compared with the single ARMA method and Elman NN model.

This study was aimed at analyzing the Brent Crude Oil Price Per Barrel. The data was extracted from the Organisation of Petroleum Exporting Countries (OPEC) Bulletin and it covered the period of January 1985 to September 2016. The Box and Jenkins approach of model identification, parameter estimation and diagnostic checking was adopted in the analysis with the aid of S-plus Package. In view of the above problem, this research was set to: explore and explain the behavior of the series; determine the best model; and forecast future values of the series. With the aid of S-Plus programming ware, R language ware; ARIMA (2,1,2) (2,0,0) [12] was reached as an optimum and parsimonious model for the series. An arithmetic increase in the price was forecast. [ABSTRACT FROM AUTHOR]

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application's behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems. [ABSTRACT FROM AUTHOR]

This paper presents a new approach to wide-area out-of-step (OOS) protection through predicting the power and rotor angle curves by the autoregressive moving-average (ARMA) method. To reduce the computation burden of ARMA, its parameters are estimated using an extended Kalman filter. The predictive feature of the proposed approach results in saving hundreds of milliseconds before the actual instability occurs, which can be served to activate appropriate countermeasures against wide-spread system instability. The OOS condition is detected using the extended equal area criterion developed for multimachine power systems, in which the single-machine infinite-bus model of the system is built using wide-area measurements. The proposed approach is adapted with the communication requirements of IEEE Std. C37.118, and thus, it can be used as a practical wide-area OOS protection scheme. The IEEE 39- and 118-bus systems are used to evaluate the efficiency of the proposed approach. The obtained results demonstrate the effectiveness of the proposed scheme in predicting OOS phenomena. [ABSTRACT FROM AUTHOR]

Accurate and timely estimations of large-scale population distributions are a valuable input for social geography and economic research and for policy-making. The most popular large-scale method to calculate such estimations uses mobile phone data. We propose a novel method, firstly based upon using a kernel density estimation (KDE) to estimate dynamic mobile phone users’ distributions at a two-hourly scale temporal resolution. Secondly, a convolutional long short-term memory (ConvLSTM) model was used in our study to predict mobile phone users’ spatial and temporal distributions for the first time at such a fine-grained temporal resolution. The evaluation results show that the predicted people’s mobility derived from the mobile phone users’ density correlates much better with the actual density, both temporally and spatially, as compared to traditional methods such as time-series prediction, autoregressive moving average model (ARMA), and LSTM.

Based on five original sentiment indicators in Hong Kong stock market, including the closed-end fund discount rate, the average first-day returns of initial public offering (IPO), the number of IPO, the market trading volume and turnover, this paper uses the principal component analysis method to calculate a comprehensive investor sentiment index. After excluding the trend and seasonal factors from the comprehensive investor sentiment index and Hong Kong Hang Seng Index by autoregressive moving average-generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) family models, this paper makes correlation analysis and Granger causality test on the residuals of ARMA-GARCH model. The study shows that ARMA-GARCH models can effectively process the autocorrelation and heteroscedasticity of investor sentiment index and Hong Kong Hang Seng Index. Also, it indicates that the Hang Seng index return is the Granger cause of investor sentiment in short terms while not in long terms. That is to say investor sentiment would be more optimistic in short terms when the Hong Kong stock market goes up, and vice versa. [ABSTRACT FROM AUTHOR]

In time series modeling consistent criteria like Bayesian Information Criterion (BIC) outperform in terms of predictability loss-efficient criteria like Akaike Information Criterion (AIC) when data are generated by a finite-order autoregressive process, and the reverse is true when data are generated by an infinite-order autoregressive process. Since in practice we don’t know the data-generating process, it is useful to have an adaptive criterion that behaves as either a consistent or just as a loss-efficient criterion, whichever performs better. Here we derive such a criterion. Moreover, our criterion is adaptive to effective sample sizes and not sensitive to maximum a priori determined order limits. [ABSTRACT FROM PUBLISHER]

Abstract: The problem of stationary and non-stationary random vibration modelling and analysis for an operating wind turbine is considered by using an acceleration vibration signal measured on a NegMicon NM52/900 fixed speed wind a turbine tower. Three stationary methods, namely Welch spectral estimation, autoregressive, and autoregressive moving average modelling, are considered, along with five non-stationary methods, namely non-parametric Wigner–Ville and spectral correlation, and parametric modelling by means of smoothness priors (SPs) time-dependent autoregressive modelling, functional series (FS) time-dependent autoregressive modelling, and adaptable functional series (AFS) time-dependent autoregressive modelling. The need for non-stationary modelling is mainly dictated by blade rotation and the wind dynamics. The results of the study confirm the cyclo-stationary and broader non-stationary nature of the random vibration, dictating the need for corresponding methods for accurate modelling and analysis. The capabilities and facets of the various methods in terms of modelling and analysing operating wind turbine random vibration are presented. [Copyright &y& Elsevier]

Active participation of rational investors in the financial markets imply its ability to select financial instruments that have the highest expected return for a given level of risk for a certain investment period. Bearing in mind that these returns are the expected values of the parameters, their values are not known in advance, so they must be forecasted. Main subject of this research refers to the use Autoregressive models (Autoregressive moving average - ARMA) in process of short term return forecasting of the Stock Exchange Index of Republic of Srpska (BIRS). The main objective of this research is to examine the efficiency of return forecasting based on autoregressive models, and trough comprehensive statistical-econometric analysis, make financial market of Republic of Srpska more informational affirmative.

The force and position data used to construct models of limb impedance are often obtained from closed-loop experiments. If the system is tested in a stiff environment, it is possible to treat the data as if they were obtained in open loop. However, when limb impedance is studied in a compliant environment, the presence of feedback cannot be ignored. While unbiased estimates of a system can be obtained directly using the prediction error method, the same cannot be said when linear regression or correlation analysis is used to fit nonparametric time- or frequency-domain models. We develop a prediction error minimization-based identification method for a nonparametric time-domain model augmented with a parametric noise model. The identification algorithm is tested on a dynamic mass–spring–damper system and returns consistent estimates of the system's properties under both stiff and compliant feedback control. The algorithm is then used to estimate the impedance of a human elbow joint in both stiff and compliant environments. [ABSTRACT FROM PUBLISHER]

This paper compares alternative methods for linear modeling of simulated time-domain responses by a rational approximation. The traditional approach based on autoregressive moving average (ARMA) is compared with two alternative approaches that have been introduced in recent years: Z-Domain Vector Fitting (ZD-VF) and Time-Domain Vector Fitting (TD-VF). Following a description of their implementation and fundamental properties, the methods are applied to the modeling of a frequency-dependent network equivalent. It is shown that TD-VF offers superior results in terms of accuracy and robustness, and the model has guaranteed stable poles. The ARMA approach requires higher orders than the others and the resulting model can be unstable. The ZD-VF approach is robust and gives a model with stable poles, but it produces incorrect simulation results when applied to truncated time-domain responses. The inaccuracy is caused mainly by the conversion of time-domain data into the z-domain. The fitting process of ARMA is faster than with the other approaches since it does not involve iterative pole relocations. [ABSTRACT FROM AUTHOR]

Biclustering is a method of grouping objects and attributes simultaneously in order to find multiple hidden patterns. When dealing with a long time series, there is a low possibility of finding meaningful clusters of whole time sequence. However, we may find more significant clusters containing partial time sequence by applying a biclustering method. This paper proposed a new biclustering algorithm for time series data following an autoregressive moving average (ARMA) model. We assumed the plaid model but modified the algorithm to incorporate the sequential nature of time series data. The maximum likelihood estimation (MLE) method was used to estimate coefficients of ARMA in each bicluster. We applied the proposed method to several synthetic data which were generated from different ARMA orders. Results from the experiments showed that the proposed method compares favorably with other biclustering methods for time series data. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents an improvement of hybrid of nonlinear autoregressive with exogenous input (NARX) model and autoregressive moving average (ARMA) model for long-term machine state forecasting based on vibration data. In this study, vibration data is considered as a combination of two components which are deterministic data and error. The deterministic component may describe the degradation index of machine, whilst the error component can depict the appearance of uncertain parts. An improved hybrid forecasting model, namely NARX–ARMA model, is carried out to obtain the forecasting results in which NARX network model which is suitable for nonlinear issue is used to forecast the deterministic component and ARMA model are used to predict the error component due to appropriate capability in linear prediction. The final forecasting results are the sum of the results obtained from these single models. The performance of the NARX–ARMA model is then evaluated by using the data of low methane compressor acquired from condition monitoring routine. In order to corroborate the advances of the proposed method, a comparative study of the forecasting results obtained from NARX–ARMA model and traditional models is also carried out. The comparative results show that NARX–ARMA model is outstanding and could be used as a potential tool to machine state forecasting. [Copyright &y& Elsevier]

Abstract: This paper proposes the hybrid model of autoregressive moving average (ARMA) and generalized autoregressive conditional heteroscedasticity (GARCH) to estimate and forecast the machine state based on vibration signal. The main idea in this study is to employ the linear ARMA model and the nonlinear GARCH model to explain the wear and fault condition of machine, respectively. The successful outcomes of the ARMA/GARCH prediction model can give obvious explanation for future states of machine, which enhance the worth of machine condition monitoring as well as condition-based maintenance in practical applications. The advance of the proposed model is verified in empirical results as applying for a real system of a methane compressor in a petrochemical plant. [Copyright &y& Elsevier]

The authors investigated the performance of the iterative Steiglitz–McBride (SM) algorithm on an autoregressive moving average (ARMA) model of signals from a fast, sparsely sampled, multiecho, chemical shift imaging (CSI) acquisition using simulation, phantom, ex vivo, and in vivo experiments with a focus on its potential usage in magnetic resonance (MR)-guided interventions. The ARMA signal model facilitated a rapid calculation of the chemical shift, apparent spin-spin relaxation time (T2*), and complex amplitudes of a multipeak system from a limited number of echoes (≤16). Numerical simulations of one- and two-peak systems were used to assess the accuracy and uncertainty in the calculated spectral parameters as a function of acquisition and tissue parameters. The measured uncertainties from simulation were compared to the theoretical Cramer–Rao lower bound (CRLB) for the acquisition. Measurements made in phantoms were used to validate the T2* estimates and to validate uncertainty estimates made from the CRLB. We demonstrated application to real-time MR-guided interventions ex vivo by using the technique to monitor a percutaneous ethanol injection into a bovine liver and in vivo to monitor a laser-induced thermal therapy treatment in a canine brain. Simulation results showed that the chemical shift and amplitude uncertainties reached their respective CRLB at a signal-to-noise ratio (SNR)≥5 for echo train lengths (ETLs)≥4 using a fixed echo spacing of 3.3 ms. T2* estimates from the signal model possessed higher uncertainties but reached the CRLB at larger SNRs and/or ETLs. Highly accurate estimates for the chemical shift (<0.01 ppm) and amplitude (<1.0%) were obtained with ≥4 echoes and for T2* (<1.0%) with ≥7 echoes. We conclude that, over a reasonable range of SNR, the SM algorithm is a robust estimator of spectral parameters from fast CSI acquisitions that acquire ≤16 echoes for one- and two-peak systems. Preliminary ex vivo and in vivo experiments corroborated the results from simulation experiments and further indicate the potential of this technique for MR-guided interventional procedures with high spatiotemporal resolution ∼1.6×1.6×4 mm3 in ≤5 s. [ABSTRACT FROM AUTHOR]

Computationally scalable and accurate estimation, prediction, and simulation of wireless communication channels is critical to the development of more adaptive transceiver algorithms. Previously, the application of autoregressive moving average (ARMA) modeling to fading processes has been complicated by ill-conditioning and nonlinear parameter estimation. This correspondence presents a numerically stable and accurate method to synthesize ARMA rational approximations of correlated Rayleigh fading processes from more complex higher order representations. Here, the problem is decomposed into autoregressive (AR) model matching followed by linear system identification. Performance is compared to that of AR, inverse discrete Fourier transform, and sum of sinusoids techniques. Also, for the first time, the finite-precision performance of different methods is compared. [ABSTRACT FROM PUBLISHER]

A fast chemical shift imaging (CSI) technique based on a multiple gradient-recalled acquisition using a small number of echoes with intentional aliasing of the reference lipid peak is studied to determine its feasibility for temperature monitoring. Simulations were implemented to find parameters where the lipid and water peaks can be measured using a Fourier-based peak fitting approach as well as using an innovative autoregressive moving average technique. A phantom consisting of 50% mayonnaise/50% lemon juice was calibrated to temperature and compared to literature values. A porcine kidney was treated ex vivo with an external laser and imaged with the CSI technique with comparisons to temperature readings from a fluoroptic monitoring system and complex phase difference (CPD) calculations. To demonstrate the technique in vivo, a Balb/c mouse with a CT26 xenograft in the subcutaneous lower back was treated using gold-coated, silica-core nanoshells heated with an 808 nm interstitial laser. Compared to standard CPD techniques using a two-dimensional fast spoiled gradient recalled echo, this technique maintains spatiotemporal resolution, has high signal-to-noise ratio and accuracy over a wide range of T2* tissue values, can separate water and lipid signals, and additionally can use the lipid peak, when present, as an internal reference. [ABSTRACT FROM AUTHOR]

Accurate and timely estimations of large-scale population distributions are a valuable input for social geography and economic research and for policy-making. The most popular large-scale method to calculate such estimations uses mobile phone data. We propose a novel method, firstly based upon using a kernel density estimation (KDE) to estimate dynamic mobile phone users' distributions at a two-hourly scale temporal resolution. Secondly, a convolutional long short-term memory (ConvLSTM) model was used in our study to predict mobile phone users' spatial and temporal distributions for the first time at such a fine-grained temporal resolution. The evaluation results show that the predicted people's mobility derived from the mobile phone users' density correlates much better with the actual density, both temporally and spatially, as compared to traditional methods such as time-series prediction, autoregressive moving average model (ARMA), and LSTM. [ABSTRACT FROM AUTHOR]

Autoregressive moving average (ARMA) processes are frequently used to model climatological time series. These tools form a broad segment of the class of linear stochastic processes. This paper summarizes formulation of nonlinear models and gives a review of a best developed type of nonlinearity. The main steps of model fitting, i.e. test for nonlinearity, model estimation, and model checking are described. Themethodology is applied to Central England annual mean temperature data. A threshold autoregressive model, a piecewise constant approximation to nonlinearity, delivers a statistically significant gain over the best fitting AR model. The forecasting function has three stable points and one limit cycle related to quasi-biennial oscillation. [ABSTRACT FROM AUTHOR]