scholarly journals Interval forecasting of time series using orderstatistics

2021 ◽  
Vol 2131 (2) ◽  
pp. 022110
Author(s):  
V Misyura ◽  
M Bogacheva ◽  
E Misyura
Keyword(s):  
Time Series ◽  
Regression Models ◽  
Robust Statistics ◽  
Traditional Approach ◽  
Model Parameters ◽  
Regularization Methods ◽  
Linear Regression Models ◽  
Predictive Algorithm ◽  
Point Forecast ◽  
Made In

Abstract In the traditional approach of obtaining time series forecasts based on the selected model, the model parameters are first estimated, then a point forecast using the obtained estimatesis made and then an interval forecast with a given probability is made. In the article the authors propose a nonparametric method for obtaining a single-stage interval forecasting of a time series based on constructing predictive and target variables sets using robust statistics and obtaining the forecast boundaries by constructing linear regression models. The predictive algorithm is based on the problems of estimating the parameters of linear multiple regression using a model regularization methods. The results of forecasting prove the expediency and effectiveness of the proposed method.

Analysis of Residual Autocorrelation in Forecasting Energy Consumption through a Java Program

2014 ◽  
Vol 962-965 ◽  
pp. 1753-1756 ◽  
Author(s):  
Renan de Oliveira Silva ◽  
Eliane da Silva Christo ◽  
Kelly Alonso Costa
Keyword(s):  
Time Series ◽  
Regression Models ◽  
Linear Regression Models ◽  
Future Planning ◽  
Efficient Tool ◽  
Java Program ◽  
To Come ◽  
Classical Linear Regression ◽  
Dynamic Quality ◽  
Made In

The study of forecasting of energy in Brazil is important for future planning, as the country has experienced crises of energy supply. And a model developed in java is an affordable and efficient tool to be used both in Brazil and in other countries. Time series analysis is highly important in many different application areas, for it allows description and modeling of a variable of interest’s behavior, thus enabling the forecasting of its future values, which serves as support for decision making. When the data used in regression analysis comprises time series, the dependency between the observations grants a dynamic quality to the regression model. In this situation, it is common to come across a problem known as residual autocorrelation, which invalidates the assumptions made about the term of error in the classical linear regression models. This paper presents a program created in Java by implementing the method of Cochrane-Orcutt for the correction of residual autocorrelation. And the application is made in the Brazilian energy final consumption forecasting.

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

scholarly journals Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 130
Author(s):  
Omar Rodríguez-Abreo ◽  
Juvenal Rodríguez-Reséndiz ◽  
L. A. Montoya-Santiyanes ◽  
José Manuel Álvarez-Alvarado
Keyword(s):  
Linear Regression ◽  
Vibration Amplitude ◽  
Regression Models ◽  
Engineering Problem ◽  
Coefficient Of Determination ◽  
Model Parameters ◽  
Gray Wolf ◽  
Linear Regression Models ◽  
Machinery Condition Monitoring ◽  
Non Linear

Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.

scholarly journals Segmented Linear Regression Models for Assessing Change in Retrospective Studies in Healthcare

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Epaminondas Markos Valsamis ◽  
David Ricketts ◽  
Henry Husband ◽  
Benedict Aristotle Rogers
Keyword(s):  
Time Series ◽  
Hip Fracture ◽  
Linear Regression ◽  
Length Of Stay ◽  
Regression Models ◽  
Retrospective Studies ◽  
Linear Regression Models ◽  
Time To Surgery ◽  
Before And After ◽  
Group Comparisons

Introduction. In retrospective studies, the effect of a given intervention is usually evaluated by using statistical tests to compare data from before and after the intervention. A problem with this approach is that the presence of underlying trends can lead to incorrect conclusions. This study aimed to develop a rigorous mathematical method to analyse temporal variation and overcome these limitations. Methods. We evaluated hip fracture outcomes (time to surgery, length of stay, and mortality) from a total of 2777 patients between April 2011 and September 2016, before and after the introduction of a dedicated hip fracture unit (HFU). We developed a novel modelling method that fits progressively more complex linear sections to the time series using least squares regression. The method was used to model the periods before implementation, after implementation, and of the whole study period, comparing goodness of fit using F-tests. Results. The proposed method offered reliable descriptions of the temporal evolution of the time series and augmented conclusions that were reached by mere group comparisons. Reductions in time to surgery, length of stay, and mortality rates that group comparisons would have credited to the hip fracture unit appeared to be due to unrelated underlying trends. Conclusion. Temporal analysis using segmented linear regression models can reveal secular trends and is a valuable tool to evaluate interventions in retrospective studies.

scholarly journals New Flexible Regression Models Generated by Gamma Random Variables with Censored Data

2016 ◽  
Vol 5 (3) ◽  
pp. 9 ◽  
Author(s):  
Elizabeth M. Hashimoto ◽  
Gauss M. Cordeiro ◽  
Edwin M.M. Ortega ◽  
G.G. Hamedani
Keyword(s):  
Regression Model ◽  
Censored Data ◽  
Survival Data ◽  
Regression Models ◽  
Empirical Distribution ◽  
Real Data ◽  
Maximum Likelihood Estimates ◽  
Standard Normal Distribution ◽  
Model Parameters ◽  
Linear Regression Models

We propose and study a new log-gamma Weibull regression model. We obtain explicit expressions for the raw and incomplete moments, quantile and generating functions and mean deviations of the log-gamma Weibull distribution. We demonstrate that the new regression model can be applied to censored data since it represents a parametric family of models which includes as sub-models several widely-known regression models and therefore can be used more effectively in the analysis of survival data. We obtain the maximum likelihood estimates of the model parameters by considering censored data and evaluate local influence on the estimates of the parameters by taking different perturbation schemes. Some global-influence measurements are also investigated. Further, for different parameter settings, sample sizes and censoring percentages, various simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We demonstrate that our extended regression model is very useful to the analysis of real data and may give more realistic fits than other special regression models. 

scholarly journals Artificial Neural Network for analyzing the chaotic time series motion: The case of the Lebanese GDP

2021 ◽  
Author(s):  
Jean-François Verne
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Phase Space ◽  
Regression Models ◽  
Chaotic Dynamic ◽  
Linear Regression Models ◽  
Large Fluctuations ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract In this paper, we propose to analyze the motion of the Lebanese GDP over the period 1950-2019. This macroeconomic aggregate reveals large fluctuations notably during the civil war period (1975-1990). By estimating the Lyapunov exponents with the Artificial Neural Network (ANN) procedure, we show that this series exhibits a strange attractor generated by a chaotic dynamic and we use the embedding procedure to shed in light the bizarre structure of such a series. Thus, the ANN method gives better results regarding prediction than other linear regression models and allows to fit with accuracy the chaotic motion followed by the Lebanese GDP in the phase space.

scholarly journals Sparse Causality Network Retrieval from Short Time Series

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Tomaso Aste ◽  
T. Di Matteo
Keyword(s):  
Time Series ◽  
Information Filtering ◽  
Linear Process ◽  
Model Parameters ◽  
Regularization Methods ◽  
Sparse Models ◽  
Short Time Series ◽  
Graphical Modelling ◽  
Short Time ◽  
Analysis Of Time Series

We investigate how efficiently a known underlying sparse causality structure of a simulated multivariate linear process can be retrieved from the analysis of time series of short lengths. Causality is quantified from conditional transfer entropy and the network is constructed by retaining only the statistically validated contributions. We compare results from three methodologies: two commonly used regularization methods, Glasso and ridge, and a newly introduced technique, LoGo, based on the combination of information filtering network and graphical modelling. For these three methodologies we explore the regions of time series lengths and model-parameters where a significant fraction of true causality links is retrieved. We conclude that when time series are short, with their lengths shorter than the number of variables, sparse models are better suited to uncover true causality links with LoGo retrieving the true causality network more accurately than Glasso and ridge.

scholarly journals PHOSPHORUS SORPTION ISOTHERMS IN SOILS OF THE SEMIARID REGION OF BRAZIL

2021 ◽  
Vol 34 (1) ◽  
pp. 166-176
Author(s):  
MONTESQUIEU DA SILVA VIEIRA ◽  
FÁBIO HENRIQUE TAVARES DE OLIVEIRA ◽  
MARCELO TAVARES GURGEL ◽  
HEMMANNUELLA COSTA SANTOS ◽  
HERNANE ARLLEN MEDEIROS TAVARES
Keyword(s):  
Regression Models ◽  
Soil Phosphorus ◽  
Sorption Isotherms ◽  
Semiarid Region ◽  
Model Parameters ◽  
Phosphorus Sorption ◽  
Linear Regression Models ◽  
Equilibrium Solutions ◽  
Phosphorus Storage ◽  
Iron And Calcium

ABSTRACT The soils of the Semiarid region of Brazil lack studies regarding sorption processes and availability of phosphorus (P). Therefore, the objective of this work was to quantify the sorption of P in ten soils representative of the Semiarid region of Brazil and correlate them with the soil phosphorus storage capacity. The P concentrations in the equilibrium solutions used to model the sorption isotherms were: 0, 5, 10, 15, 20, 30, 40, 55, 70, and 80 mg L-1 for the soils Typic Quartzipsamment (Neossolo Quartzarenico), Typic Hapludox (Latossolo Vermelho Amarelo), Typic Hapludult (Argissolo Vermelho Amarelo), Typic Quartzipsamment (Neossolo Flúvico), and Typic Dystrudept (Cambissolo Haplico); and 0, 10, 15, 25, 40, 55, 80, 100, 130, and 150 mg L-1 for the soils Typic Calciudolls (Chernossolo Rendzico), Typic Dystrudept (Cambissolo Haplico), Typic Dystrudept (Cambissolo Haplico), Typic Hapludult (Argissolo Vermelho Amarelo), and Typic Hapludert (Vertissolo Haplico). The Langmuir and Freundlich sorption isotherms were fitted to non-linear regression models and the values of the model parameters were estimated. The sorption isotherms were adequate to quantify the sorption of P in the soils of the Semiarid region of Brazil, with maximum P sorption capacity varying from 50.4 mg kg-1 to 883.5 mg kg-1. The sorption of P was higher in soils with more clayey textures, alkaline, and rich in iron and calcium, denoting the importance of evaluating the effect of these characteristics on the sorption of P in these soils.

Quantifying the effects of temporal autocorrelation on climatological regression models using geostatistical techniques

1996 ◽  
Vol 26 (5) ◽  
pp. 864-871 ◽  
Author(s):  
Ian B. Strachan ◽  
L. Edward Harvey
Keyword(s):  
Time Series ◽  
Stomatal Conductance ◽  
Regression Models ◽  
Temporal Structure ◽  
Time Lag ◽  
Ordinary Least Squares ◽  
Data Sets ◽  
Linear Regression Models ◽  
Temporal Autocorrelation ◽  
Geostatistical Techniques

When time-dependent data are used in regression models, temporal autocorrelation violates ordinary least squares assumptions and impedes their proper testing and interpretation. The problem of temporal autocorrelation is exacerbated by the uneven temporal spacing inherent in many data sets. Using simple linear regression models of stomatal conductance as examples, we compare the effectiveness of two methods for removing temporal autocorrelation from regression models (first-differencing and Cochrane–Orcutt) and we introduce the geostatistical technique of semivariograms as a method for quantifying temporal autocorrelation in uneven time series. The Cochrane–Orcutt method proved more effective than first-differencing at removing autocorrelation and produced regression models without changing the significance of the independent variables. Semivariograms were used to quantify the time dependence of the unevenly spaced stomatal conductance time series. This technique revealed the dominant autocorrelation at the minimum time lag (0.5 h) and the 24-h periodicity caused by the climatological variables used in the model. We conclude that geostatistical techniques provide a robust method for quantifying temporal structure and periodicity in unevenly spaced time series.

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