scholarly journals Estimating the turning point location in shifted exponential model of time series

2016 ◽  
Vol 44 (7) ◽  
pp. 1269-1281 ◽  
Author(s):  
Camillo Cammarota
Keyword(s):  
Time Series ◽  
Turning Point ◽  
Exponential Model ◽  
Point Location

A Bayesian Change Point Model for Historical Time Series Analysis

2004 ◽  
Vol 12 (4) ◽  
pp. 354-374 ◽  
Author(s):  
Bruce Western ◽  
Meredith Kleykamp
Keyword(s):  
Time Series ◽  
Change Point ◽  
Regression Coefficients ◽  
Simulation Methods ◽  
Historical Time ◽  
Point Location ◽  
Point Model ◽  
Political Relationships ◽  
Standard Models ◽  
Change Point Model

Political relationships often vary over time, but standard models ignore temporal variation in regression relationships. We describe a Bayesian model that treats the change point in a time series as a parameter to be estimated. In this model, inference for the regression coefficients reflects prior uncertainty about the location of the change point. Inferences about regression coefficients, unconditional on the change-point location, can be obtained by simulation methods. The model is illustrated in an analysis of real wage growth in 18 OECD countries from 1965–1992.

Zur Evolution des deutschen Konjunkturzyklus 1958-2004 / On the Evolution of German Business Cycles 1958-2004

2008 ◽  
Vol 228 (1) ◽  
Author(s):  
Ullrich Heilemann ◽  
Roland Schuhr
Keyword(s):  
Time Series ◽  
Discriminant Analysis ◽  
Business Cycles ◽  
Business Cycle ◽  
Linear Discriminant Analysis ◽  
Turning Point ◽  
Classification Methods ◽  
The West ◽  
Linear Discriminant ◽  
Macroeconomic Variables

SummaryThis paper examines changes of the (West) German business cycle from 1958 to 2004. It starts with a multivariate linear discriminant analysis (LDA) based decomposition of the cycle into 4 phases (upswing, upper turning point, downswing, lower turning point). After examining inter-cyclical changes of the cycle, i.e. changes of the weights of the 12 macroeconomic variables employed for classification, the question of intra-cyclical changes is addressed. This is done by using DLDA, a new dynamic variant of LDA which exploits the time series character of the data used to analyse changes of the multivariate structure of the cycle. The DLDA results exemplify that the transition from one to the next phase is much smoother and more continuous than might be expected. Within the sample examined these movements vary as well as the weights attributed to the classifying variables. In a methodological perspective DLDA turns out to be a promising broadening of classification methods.

A NOVEL FRAMEWORK FOR SIGNAL REPRESENTATION AND SOURCE SEPARATION: APPLICATIONS TO FILTERING AND SEGMENTATION OF BIOSIGNALS

2006 ◽  
Vol 14 (02) ◽  
pp. 169-183 ◽  
Author(s):  
GARI D. CLIFFORD
Keyword(s):  
Noise Reduction ◽  
Gradient Descent ◽  
Turning Point ◽  
Source Separation ◽  
Signal Representation ◽  
Point Location ◽  
General Technique ◽  
Periodic Oscillations ◽  
Biomedical Signals ◽  
Gaussian Kernels

A general technique for representing quasi-periodic oscillations, typical of biomedical signals, is described. Using energy thresholding and Gaussian kernels, in conjunction with a nonlinear gradient descent optimization, it is shown that significant noise reduction, compression and turning point location is possible. As such, the signal representation model can be considered a form of correlated source separation. Applications to filtering, modelling and robust ECG QT-analysis are described.

scholarly journals A Quadratic–Exponential Model of Variogram Based on Knowing the Maximal Variability: Application to a Rainfall Time Series

Mathematics ◽  
2021 ◽  
Vol 9 (19) ◽  
pp. 2466
Author(s):  
Francisco Gerardo Benavides-Bravo ◽  
Roberto Soto-Villalobos ◽  
José Roberto Cantú-González ◽  
Mario A. Aguirre-López ◽  
Ángela Gabriela Benavides-Ríos
Keyword(s):  
Time Series ◽  
Exponential Model ◽  
Complete Analysis ◽  
Rainfall Time Series ◽  
Nugget Effect ◽  
Proposed Model ◽  
Experimental Variogram ◽  
Novel Model ◽  
Hurst Exponents

Variogram models are a valuable tool used to analyze the variability of a time series; such variability usually entails a spherical or exponential behavior, and so, models based on such functions are commonly used to fit and explain a time series. Variograms have a quasi-periodic structure for rainfall cases, and some extra steps are required to analyze their entire behavior. In this work, we detailed a procedure for a complete analysis of rainfall time series, from the construction of the experimental variogram to curve fitting with well-known spherical and exponential models, and finally proposed a novel model: quadratic–exponential. Our model was developed based on the analysis of 6 out of 30 rainfall stations from our case study: the Río Bravo–San Juan basin, and was constructed from the exponential model while introducing a quadratic behavior near to the origin and taking into account the fact that the maximal variability of the process is known. Considering a sample with diverse Hurst exponents, the stations were selected. The results obtained show robustness in our proposed model, reaching a good fit with and without the nugget effect for different Hurst exponents. This contrasts to previous models, which show good outcomes only without the nugget effect.

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