scholarly journals Improvement of the Nonparametric Estimation of Functional Stationary Time Series Using Yeo-Johnson Transformation with Application to Temperature Curves

2021 ◽  
Vol 2021 ◽  
pp. 1-6
Author(s):  
Sameera Abdulsalam Othman ◽  
Haithem Taha Mohammed Ali
Keyword(s):  
Time Series ◽  
Nonparametric Estimation ◽  
Cox Model ◽  
Original Data ◽  
Stationary Time Series ◽  
Transformation Models ◽  
Response Variable ◽  
Functional Time Series ◽  
The Mean ◽  
Johnson Model

In this article, Box-Cox and Yeo-Johnson transformation models are applied to two time series datasets of monthly temperature averages to improve the forecast ability. An application algorithm was proposed to transform the positive original responses using the first model and the stationary responses using the second model to improve the nonparametric estimation of the functional time series. The Box-Cox model contributed to improving the results of the nonparametric estimation of the original data, but the results become somewhat confusing after attempting to make the transformed response variable stationary in the mean, while the functional time series predictions were more accurate using the transformed stationary datasets using the Yeo-Johnson model.

Estimation of the mean of a stationary time series by sampling

1973 ◽  
Vol 10 (02) ◽  
pp. 419-431 ◽  
Author(s):  
David R. Brillinger
Keyword(s):  
Time Series ◽  
Asymptotic Distribution ◽  
Stationary Point ◽  
Limit Theorems ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Stationary Point Process ◽  
The Mean ◽  
The Times ◽  
Sampling Times

LetX(t), – ∞ <t< ∞, be a stationary time series with meancx. Let 0 <τ1<τ2 < … <τN≦Tdenote A given sampling times in the interval (0,T]. We determine the asymptotic distribution of the estimate [X(τ1) + … +X(τN)]/Nofcxwhen the sampling times are fixed, satisfying a form of generalised harmonic analysis requirement, and when the sampling times are the times of events of a stationary point process independent of the seriesX(t). The results obtained may be viewed as non-standard central limit theorems.

scholarly journals NONPARAMETRIC ESTIMATION OF SEMIPARAMETRIC TRANSFORMATION MODELS

2016 ◽  
Vol 33 (4) ◽  
pp. 839-873 ◽  
Author(s):  
Jean-Pierre Florens ◽  
Senay Sokullu
Keyword(s):  
Nonparametric Estimation ◽  
Asymptotic Properties ◽  
Transformation Models ◽  
Dimensional Parameter ◽  
Independence Assumption ◽  
Finite Dimensional ◽  
The Mean ◽  
Semiparametric Transformation ◽  
Image Position ◽  
Nonparametric Estimates

In this paper we develop a nonparametric estimation technique for semiparametric transformation models of the form:H(Y) =φ(Z) +X′β+UwhereH,φare unknown functions,βis an unknown finite-dimensional parameter vector and the variables (Y,Z) are endogenous. Identification of the model and asymptotic properties of the estimator are analyzed under the mean independence assumption between the error term and the instruments. We show that the estimators are consistent, and a$\sqrt N$-convergence rate and asymptotic normality for$\hat \beta$can be attained. The simulations demonstrate that our nonparametric estimates fit the data well.

Estimation of the mean of a stationary time series by sampling

1973 ◽  
Vol 10 (2) ◽  
pp. 419-431 ◽  
Author(s):  
David R. Brillinger
Keyword(s):  
Time Series ◽  
Asymptotic Distribution ◽  
Stationary Point ◽  
Limit Theorems ◽  
Stationary Time Series ◽  
Stationary Time ◽  
Stationary Point Process ◽  
The Mean ◽  
The Times ◽  
Sampling Times

Let X(t), – ∞ < t < ∞, be a stationary time series with mean cx. Let 0 < τ1 < τ2 < … < τN ≦ T denote A given sampling times in the interval (0, T]. We determine the asymptotic distribution of the estimate [X(τ1) + … + X(τN)]/N of cx when the sampling times are fixed, satisfying a form of generalised harmonic analysis requirement, and when the sampling times are the times of events of a stationary point process independent of the series X(t). The results obtained may be viewed as non-standard central limit theorems.

Monitoring the Intraday Volatility Pattern

2013 ◽  
Vol 5 (2) ◽  
pp. 87-116 ◽  
Author(s):  
Robertas Gabrys ◽  
Siegfried Hörmann ◽  
Piotr Kokoszka
Keyword(s):  
Time Series ◽  
Weak Dependence ◽  
General Methodology ◽  
Functional Time Series ◽  
Change In The Mean ◽  
On Line ◽  
The Mean ◽  
Intraday Volatility ◽  
Mean Function

AbstractA functional time series consists of curves, typically one curve per day. The most important parameter of such a series is the mean curve. We propose two methods of detecting a change in the mean function of a functional time series. The change is detected on line, as new functional observations arrive. The general methodology is motivated by, and applied to, the detection of a change in the mean intraday volatility pattern. The methodology is asymptotically justified by applying a new notion of weak dependence for functional time series. It is calibrated and validated by simulations based on real intraday volatility curves.

Der Sommer und Herbst 2003 aus phänologischer Sicht | Summer and autumn 2003 from a phenological point of view

2004 ◽  
Vol 155 (5) ◽  
pp. 142-145 ◽  
Author(s):  
Claudio Defila
Keyword(s):  
Time Series ◽  
New Record ◽  
Point Of View ◽  
Late Spring ◽  
Mean Deviation ◽  
Leaf Shedding ◽  
The Mean ◽  
Very High ◽  
Selection Of

The record-breaking heatwave of 2003 also had an impact on the vegetation in Switzerland. To examine its influences seven phenological late spring and summer phases were evaluated together with six phases in the autumn from a selection of stations. 30% of the 122 chosen phenological time series in late spring and summer phases set a new record (earliest arrival). The proportion of very early arrivals is very high and the mean deviation from the norm is between 10 and 20 days. The situation was less extreme in autumn, where 20% of the 103 time series chosen set a new record. The majority of the phenological arrivals were found in the class «normal» but the class«very early» is still well represented. The mean precocity lies between five and twenty days. As far as the leaf shedding of the beech is concerned, there was even a slight delay of around six days. The evaluation serves to show that the heatwave of 2003 strongly influenced the phenological events of summer and spring.

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