Partial Autocorrelation Coefficients Research Articles

ARIMA-based Freight Forecasting and Network Optimization in E-commerce Logistics

Considering the proliferation of e-commerce platforms exemplified by Taobao and JD, the paradigm of online shopping has evolved into an integral facet of contemporary societal existence. To enhance network transport performance, it is essential to predict freight volume, assess existing capacity, and establish new sites to alleviate network pressure. This study focuses on a logistics transportation network, utilizing daily freight turnover data from January 1, 2021, to December 31, 2022. The ARIMA time series method is employed to forecast freight volumes at different sites within the e-commerce logistics network. Taking three logistics site route combinations, namely DC14→DC10, DC20→DC35, and DC25→DC62, as examples, two differencing operations are applied to meet the stationarity requirement of the ARIMA model. Autocorrelation and partial autocorrelation coefficients are used for preliminary model order determination. By minimizing the Akaike Information Criterion (AIC) value, an ARIMA (5) model is established. Single and multiple-step predictions are conducted, resulting in forecast curves for future freight volumes. Subsequently, an evaluation of the transportation network is performed, considering the importance of nodes and routes. The entropy weight method is applied to determine the weights of evaluation indicators. Importance indices for nodes and routes are calculated, leading to a ranking. For the assessment and selection of new site capabilities, Fisher's discriminant function is employed to classify different sites. The valuation level for site selection is determined, providing a scientific basis for the systematic choice of new sites.

An Integrated Framework Based on an Improved Gaussian Process Regression and Decomposition Technique for Hourly Solar Radiation Forecasting

The precise forecast of solar radiation is exceptionally imperative for the steady operation and logical administration of a photovoltaic control plant. This study proposes a hybrid framework (CBP) based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), an enhanced Gaussian process regression with a newly designed physical-based combined kernel function (PGPR), and the backtracking search optimization algorithm (BSA) for solar radiation forecasting. In the CEEMDAN-BSA-PGPR (CBP) model, (1) the CEEMDAN is executed to divide the raw solar radiation into a few sub-modes; (2) PACF (partial autocorrelation coefficient function) is carried out to pick the appropriate input variables; (3) PGPR is constructed to predict each subcomponent, respectively, with hyperparameters optimized by BSA; (4) the final forecasting result is produced by combining the forecasted sub-modes. Four hourly solar radiation datasets of Australia are introduced for comprehensive analysis and several models available in the literature are established for multi-step ahead prediction to demonstrate the superiority of the CBP model. Comprehensive comparisons with the other nine models reveal the efficacy of the CBP model and the superb impact of CEEMDAN blended with the BSA, respectively. The CBP model can produce more precise results compared with the involved models for all cases using different datasets and prediction horizons. Moreover, the CBP model is less complicated to set up and affords extra decision-making information regarding forecasting uncertainty.

Time series prediction via elastic net regularization integrating partial autocorrelation

In this paper, we propose a new elastic net regularization integrating partial autocorrelation coefficients (AEN-PAC) model for time series prediction. This model solves the inaccuracy of variable selection and parameter estimation caused by ignoring the dependence of time series in the adaptive elastic net. The proposed AEN-PAC model adds the partial autocorrelation coefficient to the penalty term of the adaptive elastic net, so that the influence of time on the data series can be well explained. Further, we prove a theorem to demonstrate that our method encourages grouping effects. Then, we convert the optimization problem of the proposed AEN-PAC model into an adaptive lasso model and propose an effective algorithm to solve it. Finally, we conduct a simulation study and empirical analysis on two time series sets. Simulation study shows that the proposed AEN-PAC model selects variable more correctly, compared with other models including Adaptive Elastic Net(AEN), Adaptive Lasso(AL), Elastic Net(EN), and Lasso. In addition, from the perspective of parameter estimation, the parameters estimated by our new model are closer to the real model. For Alibaba stock data and Nike stock data, the prediction errors are 7.07172 and 3.94916 respectively, which are smaller than other models. The results indicating that the proposed AEN-PAC model performs better in time series prediction.

Spotting micro‐movements in image sequence by introducing intelligent cubic‐LBP

Invisible to naked human eyes, micro-movements are barely noticeable. There are wide ranges of micro-movement applications from spotting subtle changes in a volcano, vascular pulse, and blood vessel to micro-expression detection. In the latter, some evil thoughts can be unveiled, resulting in identifying crooks and lawbreakers and it arises from involuntary subtle and short-duration of facial muscles movements. Precise spotting of these tiny movements is possible only when multiple aspects of temporal images are scrutinized. Meanwhile, since motions often happen in one or two directions, it is rudimentary to extract complete feature sets in textural-based approaches such as cubic Local Binary Pattern (cubic-LBP). Approaches like cubic-LBP also have imposed an unnecessary computation burden. Hence, in this research, a novel method named intelligent cubic-LBP is proposed, which incorporates Convolutional Neural Network (CNN) model. This model learns to select the useful plane(s) automatically. Apex is then detected by applying Partial Autocorrelation Coefficient (PACF) on the selected plane(s). The experimental results show significant improvement in micro-movements identification. The accuracy of the apex frame detection has elevated to 10% and 17% in the Chinese Academy of Sciences Micro-Expressions (CASME) and the CASME II databases, respectively.

Forecasting Unemployment Rate in Malaysia Using Box-Jenkins Methodology during Covid-19 Outbreak

The economic dimension has revealed that weak economic growth is one of the causes of unemployment and is related to GDP. Forecasting the unemployment rate is essential and is an important determinant of monetary policy decisions and needs to be addressed. This study is conducted to identify whether novel coronavirus 2019, COVID-19 affects Malaysia’s unemployment rate and forecast the rate for the next two years. The Box-Jenkins approach uses the Augmented Dickey-Fuller test to stabilize the data. After reducing the trend pattern of the partial autocorrelation coefficient, the ARIMA prediction method ARIMA (2,1,2) was selected as the best model to apply to the unemployment rate time series data. As a result, the projected unemployment rate graph showed a steady increase over the next two years. For future research, it is recommended to consider factors such as inflation, growth domestic product and employment to predict this value to improve results.

Using AHP and Time Series Model to Make a Better Forest Harvest Plan

Nowadays, climate change is the biggest threat to the sustainable development of our society. How to deal with greenhouse gas emissions is an essential prerequisite to solve this problem. Forest carbon sequestration is an important method to offset the release of carbon dioxide. So the balance between the value of forest products and the value of allowing trees to continue growing is closely related to the amount of carbon storage. Therefore, we are expected to find an optimal management strategy that is suitable for all the forests with different factors such as economic benefits, locations and residents’ satisfaction, so as to improve forest carbon sequestration as much as possible. Three models are established: Model I: Maximum the Carbon Sequestration; Model II: Score System via Analytic Hierarchy Process (AHP) and Entropy-Weight Method; Model III: Time Series Analysis Model and Comparison. For Model I, we aim at maximizing the carbon sequestration of a forest and collect a series of biological parameters throughout the America. We start with describing the relationship between forest growing stock and carbon sequestration via the conversion ratio. Afterwards, we introduce the S-size Logistic curve to fit the growing stock volume and set different harvest and plant rate to observe the change of growing stock volume so that find an optimal harvest or plant rate to maximize the carbon sequestration amount. The transition point is found as 4% for harvest rate and also 4% for plant rate. We note that although the values of these two parameters are identical, it would arouse a significant difference because they are completely different process for a forest. For Model II, we take citizen attitude and timber value into consideration besides carbon sequestration. With the existing data in Model I and the data of new factors from literatures, we construct a score system of different strategies through analytic hierarchy process and entropy method. As a result, the optimal harvest rate decreases although timber value is included, because the local citizens interviewed in the literature showed a high-level anti-harvest attitude, which is consistent with the output of our model. For Model III, we establish a time series of carbon sequestration (or growing stock, they can be interconverted without difficulty). After recognizing the specific type (ARIMA type) of this time series by solving difference equation, the data in the subsequent years can be predicted. Then, we make the varication of the autocorrelation coefficient and partial autocorrelation coefficient (ACF and PACF) the ensure that the result is reliable and dose make sense. The models constructed in this paper make full use of the data obtained from website and literatures to describe and visualize the results. They are validated by sensitivity analysis and can be widely applied to evaluate the different situations. At the end, we provide a two-page newspaper for the authorities and local residents. According to the output in this paper, we finally draw a conclusion that an appropriate harvest rate is advantageous for both forest and society, and this phenomenon is called tending felling or reproduction felling, which is widely applied in managing forest.

Decomposition-based teleconnection between monthly streamflow and global climatic oscillation

Hydrometeorological teleconnections play a vital role in hydrological processes, as they can reflect the influence of large-scale atmospheric circulations. Understanding and accessing such teleconnections can help reveal the hydrometeorological phenomenon and its associated mechanisms. Therefore, a decomposition-based teleconnection framework (DBTF) is developed to identify the hydrological processes related to the teleconnections under climate change. It consists of three modules, namely, hydrological series decomposition, teleconnection patterns decomposition, and attribution analysis. Specifically, the streamflows are separated into the stationary and nonstationary components using the SSA decomposition technique. Then, the teleconnection patterns (ENSO, AO, PDO, and Sunspots) are decomposed into multi-layer signals by the EEMD decomposition technique. The multiple complex correspondences of different components from hydrological and climate variables are studied by factorial analysis, wavelet coherence transformation, maximum information coefficient, and the partial autocorrelation coefficients. Basins from the humid, semi-humid and semiarid, and arid climate zones are estimated by the framework. The results show that: (1) high-frequency of teleconnection patterns has the highest correlation with the stationary components of streamflow, while low-frequency and trend have the most considerable association with the nonstationary components; (2) the stationary components of streamflow show more pronounced correlations with the teleconnection patterns than the nonstationary components; (3) teleconnection patterns demonstrate more substantial on the decomposed components than the raw streamflow. The contribution is to provide new insight into mechanisms in the effects of hydrometeorological teleconnections on hydrological variations.

Efficient Bayesian PARCOR approaches for dynamic modeling of multivariate time series

A Bayesian lattice filtering and smoothing approach is proposed for fast and accurate modeling and inference in multivariate non‐stationary time series. This approach offers computational feasibility and interpretable time‐frequency analysis in the multivariate context. The proposed framework allows us to obtain posterior estimates of the time‐varying spectral densities of individual time series components, as well as posterior measurements of the time‐frequency relationships across multiple components, such as time‐varying coherence and partial coherence. The proposed formulation considers multivariate dynamic linear models (MDLMs) on the forward and backward time‐varying partial autocorrelation coefficients (TV‐VPARCOR). Computationally expensive schemes for posterior inference on the multivariate dynamic PARCOR model are avoided using approximations in the MDLM context. Approximate inference on the corresponding time‐varying vector autoregressive (TV‐VAR) coefficients is obtained via Whittle's algorithm. A key aspect of the proposed TV‐VPARCOR representations is that they are of lower dimension, and therefore more efficient, than TV‐VAR representations. The performance of the TV‐VPARCOR models is illustrated in simulation studies and in the analysis of multivariate non‐stationary temporal data arising in neuroscience and environmental applications. Model performance is evaluated using goodness‐of‐fit measurements in the time‐frequency domain and also by assessing the quality of short‐term forecasting.

Methodology for Analyzing and Predicting the Runoff and Sediment into a Reservoir

With the rapid economic growth in China, a large number of hydropower projects have been planned and constructed. The sediment deposition of the reservoirs is one of the most important disputes during the construction and operation, because there are many heavy sediment-laden rivers. The analysis and prediction of the runoff and sediment into a reservoir is of great significance for reservoir operation. With knowledge of the incoming runoff and sediment characteristics, the regulator can adjust the reservoir discharge to guarantee the water supply, and flush more sediment at appropriate times. In this study, the long-term characteristics of runoff and sediment, including trend, jump point, and change cycle, are analyzed using various statistical approaches, such as accumulated anomaly analysis, the Fisher ordered clustering method, and Maximum Entropy Spectral Analysis (MESA). Based on the characteristics, a prediction model is established using the Auto-Regressive Moving Average (ARIMA) method. The whole analysis and prediction system is applied to The Three Gorges Project (TGP), one of the biggest hydropower-complex projects in the world. Taking hydrologic series from 1955 to 2010 as the research objectives, the results show that both the runoff and the sediment are decreasing, and the reduction rate of sediment is much higher. Runoff and sediment into the TGP display cyclic variations over time, with a cycle of about a decade, but catastrophe points for runoff and sediment appear in 1991 and 2001, respectively. Prediction models are thus built based on monthly average hydrologic series from 2003 to 2010. ARIMA (1, 1, 1) × (1, 1, 1)12 and ARIMA (0, 1, 1) × (0, 1, 1)12 are selected for the runoff and sediment predictions, respectively, and the parameters of the models are also calibrated. The analysis of autocorrelation coefficients and partial autocorrelation coefficients of the residuals indicates that the models built in this study are feasible for representing and predicting the runoff and sediment inflow into the TGP with a high accuracy.

Fukunaga-Koontz feature transformation for statistical structural damage detection and hierarchical neuro-fuzzy damage localisation

Considering jointly damage sensitive features (DSFs) of signals recorded by multiple sensors, applying advanced transformations to these DSFs and assessing systematically their contribution to damage detectability and localisation can significantly enhance the performance of structural health monitoring systems. This philosophy is explored here for partial autocorrelation coefficients (PACCs) of acceleration responses. They are interrogated with the help of the linear discriminant analysis based on the Fukunaga-Koontz transformation using datasets of the healthy and selected reference damage states. Then, a simple but efficient fast forward selection procedure is applied to rank the DSF components with respect to statistical distance measures specialised for either damage detection or localisation. For the damage detection task, the optimal feature subsets are identified based on the statistical hypothesis testing. For damage localisation, a hierarchical neuro-fuzzy tool is developed that uses the DSF ranking to establish its own optimal architecture. The proposed approaches are evaluated experimentally on data from non-destructively simulated damage in a laboratory scale wind turbine blade. The results support our claim of being able to enhance damage detectability and localisation performance by transforming and optimally selecting DSFs. It is demonstrated that the optimally selected PACCs from multiple sensors or their Fukunaga-Koontz transformed versions can not only improve the detectability of damage via statistical hypothesis testing but also increase the accuracy of damage localisation when used as inputs into a hierarchical neuro-fuzzy network. Furthermore, the computational effort of employing these advanced soft computing models for damage localisation can be significantly reduced by using transformed DSFs.

An ARMA(1,1) model for monthly stream flows

The partial auto-correlation coefficients of most of the series of monthly stream flows recorded in Turkey of lag greater than one are within the 99% confidence band while the lag-one partial correlation coefficient is highly significant. An ARMA(1,1) model to compute synthetic monthly flows up to 5000 years is developed, which is different from the ones presented in relevant sources. Having observed mostly skewed standardized variates, they are normalized by the 3-parameter log-normal distribution whose parameters are computed by the method of maximum-likelihood (LN3-ML). The two parameters of the ARMA(1,1) model are computed so as to minimize the sum of squares of differences of the observed and computed standardized–normalized variates rather than minimizing the sum of squares of the random component terms. Application of the developed model on the 64-year-long monthly flow series of the unregulated Kizilirmak River in Turkey is given here as an example, which reveals plausible results.

Improved damage detectability in a wind turbine blade by optimal selection of vibration signal correlation coefficients

The central message of this article is that for robust and efficient damage detection the damage sensitive features should be selected optimally in a systematic way such that only these features that contribute the most to damage detectability be retained. Furthermore, suitable transformations of the original features may also enhance damage detectability. We explore these principles using data from a wind turbine blade. Several damage extent scenarios are introduced non-destructively. Partial autocorrelation coefficients are proposed as vibration-based damage sensitive features. Scores calculated with principal component analysis of partial autocorrelation coefficients are the transformed damage sensitive features. Statistical distances between the damage sensitive feature subsets estimated from the healthy and a reference damage state are calculated with respect to a statistical threshold as a measure of optimality. The fast forward method and a genetic algorithm are used to optimize the detectability of damage by damage sensitive feature selection. The comparison between the two methods points out that fast forward offers a comparable performance at a lower computational cost. The classifiers based on the optimal features are tested on data from several previously unseen healthy and damaged cases and across a range of statistical detection thresholds. It is demonstrated that the selected principal component analysis scores of the partial autocorrelation coefficients are superior compared to the initial features and allow identifying small damage confidently.

Modelling the root zone soil moisture using artificial neural networks, a case study

Surface soil moisture constitutes a major component in the Earth’s water cycle. In many cases, modelling and predicting soil moisture represent a serious problem in water resources field due to the problematic measurements or lack of measurements, etc. Data-driven models such as artificial neural networks (ANN) have been characterized as a robust tool to overcome these shortcomings. This study aims to identify the optimum ANNs to model the root zone soil moisture (up to 2 m depth) in the upper reach of the Spree River using the synthetic soil moisture data from SWAT model. Thus, three different approaches were developed and compared to determine the highest performing method. These networks can be broadly categorized into dynamic, static, and statistical neural networks, which are layer recurrent network (LRN), feedforward (FF), and radial basis networks, respectively. Data sets of precipitation and antecedent soil moisture were selected based on quantification of cross-, auto-, and partial autocorrelation coefficients to represent the best behaviour of root soil moisture. The time series data were subdivided into two subsets: one for network training and the second for network testing. The determination coefficient (R 2), root-mean-square error, and Nash–Sutcliffe efficiency were employed to test the goodness of fit between the actual and modelled data. Results show that, among the used methods, the LRN and FF networks have the top performance criteria, showing a reliable ability to be used as estimator for the soil moisture in this catchment.

A comparative assessment of adaptive neuro-fuzzy inference system, artificial neural network and regression for modelling stage-discharge relationship

This study aims to evaluate the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), two and multi-linear regression (MLR) for modelling stage-discharge (S-D) relationship and the results are compared with those of the S-D rating curves. S-D relationship was carried out using discharge and water level (stage) data during 2011-2012 water year in Tangeh Lavij hydrometry station with area of 104 km² located in Mazandaran Province. The partial autocorrelation coefficients were estimated for the daily stage and three input combinations based on current and previous daily stage were selected as inputs to the ANFIS, ANN and MLR models. ANN, ANFIS and MLR methods with three input combinations of stage and conventional S-D relationship were used for modelling S-D relationship. The results showed that artificial intelligence methods were found to be superior to regression and conventional S-D relationship and ANFIS are the most suitable method for modelling S-D relationship.

Exponential decay rate of partial autocorrelation coefficients of ARMA and short-memory processes

We present a short proof of the fact that the exponential decay rate of partial autocorrelation coefficients of a short-memory process, in particular an ARMA process, is equal to the exponential decay rate of the coefficients of its infinite autoregressive representation.

Experimental damage detection in a wind turbine blade model using principal components of response correlation functions

The utilization of vibration signals for structural damage detection (SDD) is appealing due to the strong theoretical foundation of such approaches, ease of data acquisition and processing efficiency. Different methods are available for defining damage sensitive features (DSFs) based on vibrations, such as modal analysis or time series methods. The present paper proposes the use of partial autocorrelation coefficients of acceleration responses as DSFs. Principal component (PC) analysis is used to transform the initial DSFs to scores. The resulting scores from the healthy and damaged states are used to select the PCs which are most sensitive to damage. These are then used for making decisions about the structural state by means of statistical hypothesis testing conducted on the scores. The approach is applied to experiments with a laboratory scale wind turbine blade (WTB) made of glass-fibre reinforced epoxy composite. Damage is non-destructively simulated by attaching small masses and the WTB is excited with the help of an electrodynamic shaker using band-limited white noise. The SDD results for the selected subsets of PCs show a clear improvement of the detectability of early damages compared to other DSF selections.

Generalised Partial Autocorrelations and the Mutual Information between Past and Future

The paper introduces the generalised partial autocorrelation (GPAC) coefficients of a stationary stochastic process. The latter are related to the generalised autocovariances, the inverse Fourier transform coefficients of a power transformation of the spectral density function. By interpreting the generalised partial autocorrelations as the partial autocorrelation coefficients of an auxiliary process, we derive their properties and relate them to essential features of the original process. Based on a parameterisation suggested by Barndorff-Nielsen and Schou (1973) and on Whittle likelihood, we develop an estimation strategy for the GPAC coefficients. We further prove that the GPAC coefficients can be used to estimate the mutual information between the past and the future of a time series.

A Feature Extraction Method Using Empirical Mode Decomposition and Histogram Modeling for Robust Bearing Fault Diagnosis

This paper proposes a method using empirical mode decomposition (EMD) and histogram modeling for bearing defects of a low-speed rotational induction motor. The proposed method minimizes signal distortion caused by noise using the EMD and conducts histogram envelope modeling by normalizing a signal. Then, the method extracts and selects unique features of each fault using partial autocorrelation coefficients (PARCOR) and distance evaluation technique (DET). Using the extracted features as inputs, support vector Received(July 10, 2014), Review request(July 11, 2014), Review Result(1st: July 24, 2014) Accepted(December 10, 2014) Publish(December 31, 2014) 689-749 School of Electrical Engineering, University of Ulsan, Nam-gu, Ulsan, Korea. email: fahmid.farid@gmail.com 689-749 School of Electrical Engineering, University of Ulsan, Nam-gu, Ulsan, Korea. email: nasha0339@gmail.com (Corresponding Author) 689-749 School of Electrical Engineering, University of Ulsan, Nam-gu, Ulsan, Korea. email: jmkim07@ulsan.ac.kr * This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology(NRF-2012R1A1A2043644) A Feature Extraction Method Using Empirical Mode Decomposition and Histogram Modeling for Robust Bearing Fault Diagnosis Copyright c 2014 SERSC 282 regression (SVR) classifies inner, outer, and roller faults of a bearing. For the optimal classification performance, we vary a variable of the kernel function of SVR ranging from 0.01 to 1.0 and the number of features ranging from 2 to 250. Experimental results indicate that the proposed method having a parameter of 0.55 and 45 features outperforms conventional fault classification methods, yielding an average of 95% classification performance.

음향 방출 신호와 히스토그램 모델링을 이용한 유도전동기의 베어링 결함 검출

본 논문에서는 저속으로 회전하는 유도 전동기의 베어링 결함을 검출하기 위해 음향 방출 신호와 히스토그램 모델링을 이용하는 방법을 제안한다. 제안한 방법은 정규화된 결함 신호가 구성하는 히스토그램의 포락선을 모델링하여, 부분 상관 계수와 DET(Distance Evaluation Technique) 기법을 이용하여 결함 유형별 고유한 특징을 추출 및 선택한다. 추출된 특징을 SVR(Support Vector Regression) 분류기의 입력으로 사용하여 베어링의 내륜, 외륜 및 롤러 결함을 분류한다. 최적의 분류 성능을 위해 SVR 커널함수의 매개변수를 0.01에서 1.0까지 변화시키고, 특징 개수는 2에서 150까지 변화시키면서 실험한 결과, 0.64-0.65의 매개변수와 75개의 특징 개수에서 제안한 방법은 약 91%의 분류 성능을 보였고, 또한 기존의 결함 분류 알고리즘보다 높은 분류 성능을 보였다. This paper proposes a fault detection method for low-speed rolling element bearings of an induction motor using acoustic emission signals and histogram modeling. The proposed method performs envelop modeling of the histogram of normalized fault signals. It then extracts and selects significant features of each fault using partial autocorrelation coefficients and distance evaluation technique, respectively. Finally, using the extracted features as inputs, the support vector regression (SVR) classifies bearing's inner, outer, and roller faults. To obtain optimal classification performance, we evaluate the proposed method with varying an adjustable parameter of the Gaussian radial basis function of SVR from 0.01 to 1.0 and the number of features from 2 to 150. Experimental results show that the proposed fault identification method using 0.64-0.65 of the adjustable parameter and 75 features achieves 91% in classification performance and outperforms conventional fault diagnosis methods as well.

Reliability and Comparability of the Accelerometer and the Linear Position Measuring Device in Resistance Training

The purpose of this study was to determine the intermachine reliability attained from devices used to measure the common variables in sports performance. Repeatability conditions were established by creating a similar set of conditions under which the measurements were taken from both devices. The objectives of this research were to demonstrate the reliability between two devices in a bench press movement--the linear position measuring device (LPM) isoinertial dynamometer (T-Force) and the 3D (Myotest) accelerometer (AC)--and to compare the existing correlations between maximum velocity, maximum estimated strength, and peak power estimate variables in the bench press exercise. Forty bench press exercise trials were analyzed simultaneously, performed by 3 different subjects (age: 26.74 ± 1.2 years, height: 175.74 ± 4.04 cm, weight: 78.7 ± 3.35 kg) at maximum velocity (25 kg additional load). Three simple linear regression models were developed, supplied by the LPM on the basis of the AC data. The assumption of independence of errors was compared by means of the Durban-Watson test, and partial autocorrelation coefficients were established at an overall p < 0.05 significance level. It has not been possible to confirm the presence of a general correlation between the measurements of both devices. Regarding the assumption of independence of errors, the presence of generalized autocorrelation was confirmed. Linear regression analysis revealed an intermachine correlation in one of the nonconclusive cases, (peak power) variable and subject 1, r (10) = 0.640, p = 0.024. No partial autocorrelation was found. The devices should not be used interchangeably as instruments.