PREDICTION OF TIME SERIES USING MULTIPLE REGRESSION TECHNIQUES AND SEAKEEPING APPLICATIONS

1963 ◽  
Author(s):  
Loren D. Enochson
Keyword(s):  
Time Series ◽  
Multiple Regression ◽  
Regression Techniques

TIME SERIES ANALYSIS IN AGROMETEOROLOGICAL PROBLEMS WITH EMPHASIS ON SPECTRUM ANALYSIS

1967 ◽  
Vol 47 (5) ◽  
pp. 477-491
Author(s):  
R. L. Desjardins
Keyword(s):  
Time Series ◽  
Environmental Factors ◽  
Time Series Analysis ◽  
Multiple Regression ◽  
Spectrum Analysis ◽  
Meteorological Parameters ◽  
Regression Technique ◽  
Cross Spectrum ◽  
Time Series Studies ◽  
Regression Techniques

This paper reviews the principles and use of spectrum analysis and discusses the effectiveness of combining multiple regression techniques with cross-spectrum analysis in time series studies. This method is useful in comparing the variations of an empirical index or the response of different instruments to environmental factors as a function of time. The variability of daily latent-evaporation estimates from a regression technique is examined and compared with evaporation readings from atmometers. A comparison is made of the variation in evaporation from two types of small atmometers and two types of tank evaporimeters. All four instruments appear to respond to meteorological parameters similarly at Swift Current and Ottawa.

Some issues in the simulation of ANOVA by multiple regression techniques

1975 ◽  
Vol 5 (1) ◽  
pp. 199-207 ◽  
Author(s):  
John T. McArthur ◽  
Barry J. Fraser ◽  
Leo H. T. West
Keyword(s):  
Multiple Regression ◽  
Regression Techniques

scholarly journals The optimal number of partial least squares components in genomic selection for pork pH

2017 ◽  
Vol 47 (1) ◽  
Author(s):  
Fernanda Gomes da Silveira ◽  
Darlene Ana Souza Duarte ◽  
Lucas Monteiro Chaves ◽  
Fabyano Fonseca e Silva ◽  
Ivan Carvalho Filho ◽  
...  
Keyword(s):  
Least Squares ◽  
Genomic Selection ◽  
Multiple Regression ◽  
Partial Least Squares ◽  
Predictive Accuracy ◽  
Principal Component ◽  
Theoretical Background ◽  
Optimal Number ◽  
Number Of Components ◽  
Regression Techniques

ABSTRACT: The main application of genomic selection (GS) is the early identification of genetically superior animals for traits difficult-to-measure or lately evaluated, such as meat pH (measured after slaughter). Because the number of markers in GS is generally larger than the number of genotyped animals and these markers are highly correlated owing to linkage disequilibrium, statistical methods based on dimensionality reduction have been proposed. Among them, the partial least squares (PLS) technique stands out, because of its simplicity and high predictive accuracy. However, choosing the optimal number of components remains a relevant issue for PLS applications. Thus, we applied PLS (and principal component and traditional multiple regression) techniques to GS for pork pH traits (with pH measured at 45min and 24h after slaughter) and also identified the optimal number of PLS components based on the degree-of-freedom (DoF) and cross-validation (CV) methods. The PLS method out performs the principal component and traditional multiple regression techniques, enabling satisfactory predictions for pork pH traits using only genotypic data (low-density SNP panel). Furthermore, the SNP marker estimates from PLS revealed a relevant region on chromosome 4, which may affect these traits. The DoF and CV methods showed similar results for determining the optimal number of components in PLS analysis; thus, from the statistical viewpoint, the DoF method should be preferred because of its theoretical background (based on the "statistical information theory"), whereas CV is an empirical method based on computational effort.

Heredity and Environment in Phoneme Articulation: Hereditary and Environmental Contributions to Articulation Proficiency

1995 ◽  
Vol 44 (2) ◽  
pp. 63-73
Author(s):  
W. E. Dixon ◽  
A. P. Matheny ◽  
S. R. Mohr
Keyword(s):  
Factor Analysis ◽  
Multiple Regression ◽  
Behavioral Genetics ◽  
Environmental Influences ◽  
Individual Factors ◽  
Factor Scores ◽  
Genetic Components ◽  
Sources Of Variation ◽  
Regression Techniques ◽  
The Individual

AbstractLarge twin samples and recent applications of multiple regression techniques to behavioral genetics methodology makes possible evaluation of genetic and environmental contributions to the articulation proficiency of individual phonemes. Factor analysis of the articulation scores from 256 MZ and DZ twins and 124 of their non-twin siblings (all children ranged from 2; 11 to 9; 8 years) were conducted to reduce a 50-item articulation test to a more manageable set of five articulation factors. The twins' factor scores were then analyzed using multiple regression procedures to determine the extent to which the individual factors resulted from genetic and/or environmental influences. The /r/ and /∫, t∫, dƺ/ factors were found to have strong genetic components, while the /l, j, w/ factor was found to be strongly influenced by environmental sources of variation.

FORECASTING CONSUMER PRICE INDEX (CPI) USING TIME SERIES MODELS AND MULTI REGRESSION MODELS (ALBANIA CASE STUDY)

2018 ◽  
Author(s):  
Eralda Gjika Dhamo ◽  
Llukan Puka ◽  
Oriana Zaçaj
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Multiple Regression ◽  
Consumer Price Index ◽  
Multiple Regression Model ◽  
Time Series Models ◽  
Financial Indicators ◽  
Consumer Prices ◽  
Approach Time

In this work we analyse the CPI index as the official index to measure inflation in Albania, Harmo-nized Indices of Consumer Prices (HICPs) as the bases for comparative measurement of inflation in European countries and other financial indicators that may affect CPI. This study is an attempt to model CPI based on combination of multiple regression model with time series forecasting models. In the first approach, time series models were used directly on the CPI time series index to obtain the forecast. In the second approach, the time series models (SARIMA, ETS) were used to model and simulate forecast for each subcomponent with significant correlation to CPI and then use the multiple regression model to obtain CPI forecast. The projection of this indicator is important for understand-ing the country's economic and social development. This study helps researchers in the field of time series modeling, economic analysis and investments.

scholarly journals Explicit Modeling of Meteorological Explanatory Variables in Short-Term Forecasting of Maximum Ozone Concentrations via a Multiple Regression Time Series Framework

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1304
Author(s):  
Sigfrido Iglesias-Gonzalez ◽  
Maria E. Huertas-Bolanos ◽  
Ivan Y. Hernandez-Paniagua ◽  
Alberto Mendoza
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Multiple Regression ◽  
Urban Areas ◽  
Moving Average ◽  
Time Series Forecasting ◽  
Air Pollutant ◽  
Short Term ◽  
Ozone Level ◽  
Ozone Concentrations

Statistical time series forecasting is a useful tool for predicting air pollutant concentrations in urban areas, especially in emerging economies, where the capacity to implement comprehensive air quality models is limited. In this study, a general multiple regression with seasonal autoregressive moving average errors model was estimated and implemented to forecast maximum ozone concentrations with a short time resolution: overnight, morning, afternoon and evening. In contrast to a number of short-term air quality time series forecasting applications, the model was designed to explicitly include the effects of meteorological variables on the ozone level as exogenous variables. As the application location, the model was constructed with data from five monitoring stations in the Monterrey Metropolitan Area of Mexico. The results show that, together with structural stochastic components, meteorological parameters have a significant contribution for obtaining reliable forecasts. The resulting model is an interpretable, useful and easily implementable model for forecasting ozone maxima. Moreover, it proved to be consistent with the general dynamics of ozone formation and provides a suitable platform for forecasting, showing similar or better performance compared to models in other existing studies.

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