scholarly journals Transfer Function Models with Time-Varying Coefficients

2012 ◽  
Vol 2012 ◽  
pp. 1-31 ◽  
Author(s):  
Maria Sílvia de A. Moura ◽  
Pedro A. Morettin ◽  
Clélia M. C. Toloi ◽  
Chang Chiann
Keyword(s):  
Transfer Function ◽  
Least Squares Method ◽  
Estimation Procedure ◽  
Time Varying ◽  
Function Model ◽  
Varying Coefficients ◽  
Wavelet Expansions ◽  
Time Varying Coefficients ◽  
Function Models ◽  
Transfer Function Models

We consider a transfer function model with time-varying coefficients. We propose an estimation procedure, based on the least squares method and wavelet expansions of the time-varying coefficients. We discuss some statistical properties of the estimators and assess the validity of the methodology through a simulation study. We also present an application of the proposed procedure to a real pair of series.

scholarly journals Peramalan Kebutuhan Daya Listrik Menggunakan Model ARIMA dan Fungsi Transfer (Studi Kasus: PT. PLN (Persero) Area Sumbawa)

2018 ◽  
Vol 4 (2) ◽  
pp. 122-127
Author(s):  
Mikhratunnisa Mikhratunnisa ◽  
Tri Susilawati
Keyword(s):  
Economic Development ◽  
Transfer Function ◽  
Arima Model ◽  
Transfer Function Model ◽  
Customer Demand ◽  
Function Model ◽  
Electrical Capacity ◽  
The Right ◽  
Function Models ◽  
Transfer Function Models

Energy is one of the basic need of human being. One of the vital energy is electricity. The need of electricity in NTB is increase along with the citizen economic development in NTB especially in Sumbawa regency. Therefore, there is a need for the right way in adjusting the amount of electrical capacity to match customer demand. One way that can be done is to forecast/ predict the need for electricity. The forecast can be used by using the ARIMA and Transfer Function models. The results of the study show that using the ARIMA model is estimated to require electricity in 2018 experienced an increase of 18,21% from the previous year, while using the transfer function model is estimated to increase by 18,18% from the previous year.

scholarly journals Measuring the Effect of Government Response on COVID-19 Pandemic: Empirical Evidence from Japan

COVID ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 276-287
Author(s):  
Rui Wang
Keyword(s):  
Least Squares Method ◽  
Empirical Finding ◽  
Japanese Government ◽  
Time Varying ◽  
Multiple Series ◽  
Government Response ◽  
Varying Coefficients ◽  
Flexible Least Squares ◽  
The Government ◽  
Time Varying Coefficients

The basic approach of this research is to use an estimated series of effective reproduction number Rt and multiple series of index from Oxford COVID-19 Government Response Tracker (OxCGRT) to measure the effect of Japanese government’s response on COVID-19 epidemic by running a time-varying regression with flexible least squares method. Then, we use estimated series of time-varying coefficients obtained from the previous step as proxy variables for the government response’s effect and run stepwise regressions with policy indicators of OxCGRT to identify which specific policy can mitigate the spreading of the COVID-19 epidemic in Japan. The main finding is that the response of Japanese government on COVID-19 epidemic is basically effective. However, the effect of Japanese government’ policy is gradually weakening. Under our identification scheme, we find that policies of quarantine and movement restrictions are still most effective, but policies of public health system do not show much effectiveness in the regression analysis. Another important empirical finding is that policies of economic support are effective in reducing the spread of COVID-19. Within the framework of empirical strategy proposed in this paper, the conclusion should be explained in the context of the socio-political and health situation in Japan, but the methodology is assumed to be applicable to other countries and regions in the analysis of government performance of response to COVID-19.

scholarly journals PERAMALAN JUMLAH KUNJUNGAN WISATAWAN MANCANEGARA DI KEPULAUAN RIAU DENGAN MENGGUNAKAN MODEL FUNGSI TRANSFER

2020 ◽  
Vol 9 (2) ◽  
pp. 152-161
Author(s):  
Tamura Rolasnirohatta Siahaan ◽  
Rukun Santoso ◽  
Alan Prahutama
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Transfer Function Model ◽  
Analysis Model ◽  
Function Model ◽  
The Past ◽  
Causal Approach ◽  
Function Models ◽  
Related Time Series ◽  
Transfer Function Models

Transfer function models is a data analysis model that combines time series and causal approach, in another words, transfer function models is a method that ilustrates that the predicted value in teh future is affected by the past value time series and based on one or more related time series. In this research, an analysis of the number of tourist arrival and rainfall in several regions in Kepulauan Riau from January 2013 until December 2017 was aimed at obtaining a transfer function model and forecasting the number of tourist arrival in several regions of the Kepulauan Riau for next periods. Based on the result of the analysis, rainfall in Tanjung Pinang does not affect the visit of tourist with the values of MAPE is 13,63494%. Rainfall in Batam also does not affect the visit of tourist with the values of MAPE is 7,977151%. While in Tanjung Balai Karimun, tourist arrivals was affected by rainfall with the values of MAPE is 10,32777%.

scholarly journals Polynomial and Wavelet-Type Transfer Function Models to Improve Fisheries’ Landing Forecasting with Exogenous Variables

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1082
Author(s):  
Eliana Vivas ◽  
Héctor Allende-Cid ◽  
Rodrigo Salas ◽  
Lelys Bravo
Keyword(s):  
Transfer Function ◽  
Large Scale ◽  
Variable Coefficients ◽  
Climatic Variables ◽  
Basic Knowledge ◽  
Wavelet Expansion ◽  
Northern Chile ◽  
Varying Coefficients ◽  
Function Models ◽  
Transfer Function Models

It is well known that environmental fluctuations and fishing efforts modify fishing patterns in various parts of the world. One of the most affected areas is northern Chile. The reduction of the gaps in the implementation of national fisheries’ management policies and the basic knowledge that supports the making of such decisions are crucial. That is why in this research, a transfer function method with variable coefficients is proposed to forecast monthly disembarkation of anchovies and sardines in northern Chile, taking into account the incidence of large-scale climatic variables on landings. The method uses a least squares procedure and wavelets to expand the coefficients of the transfer function. Linear estimators of the time varying coefficients are proposed, followed by a truncation of the wavelet expansion up to an appropriate scale. Finally, the estimators for the transfer function coefficients are obtained by using the inverse wavelet transformation. Research results suggest that the transfer function models with variable coefficients fit the behavior of the anchovies’ landing with great accuracy, while the use of transfer function models with constant coefficients fits sardines’ landings better. Both fisheries’ landings could be explained to a large extent from the large scale climatic variables.

scholarly journals The frequency response of the object, and the estimation of the parameters of the simplified transfer function model

2019 ◽  
Vol 28 ◽  
pp. 01047
Author(s):  
Konrad Dudziak ◽  
Krzysztof Stawicki ◽  
Andrzej Brykalski
Keyword(s):  
System Identification ◽  
Transfer Function ◽  
Frequency Response ◽  
Transfer Function Model ◽  
Function Model ◽  
Time Constants ◽  
Modified Method ◽  
Space Point ◽  
Function Models ◽  
Transfer Function Models

The article presents a comparison of the modified method of the moments and the System Identification Toolbox ™ for the purpose of determining equivalent parameters (depending on the space point, time constants, delays, row of inertia) of simplified transfer function models.

scholarly journals The VIT Transform Approach to Discrete-Time Signals and Linear Time-Varying Systems

Eng ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 99-125
Author(s):  
Edward W. Kamen
Keyword(s):  
Discrete Time ◽  
Initial Conditions ◽  
Linear Time ◽  
Order Term ◽  
Initial Time ◽  
Time Varying ◽  
Varying Coefficients ◽  
Time Signals ◽  
Time Varying Systems ◽  
Time Varying Coefficients

A transform approach based on a variable initial time (VIT) formulation is developed for discrete-time signals and linear time-varying discrete-time systems or digital filters. The VIT transform is a formal power series in z−1, which converts functions given by linear time-varying difference equations into left polynomial fractions with variable coefficients, and with initial conditions incorporated into the framework. It is shown that the transform satisfies a number of properties that are analogous to those of the ordinary z-transform, and that it is possible to do scaling of z−i by time functions, which results in left-fraction forms for the transform of a large class of functions including sinusoids with general time-varying amplitudes and frequencies. Using the extended right Euclidean algorithm in a skew polynomial ring with time-varying coefficients, it is shown that a sum of left polynomial fractions can be written as a single fraction, which results in linear time-varying recursions for the inverse transform of the combined fraction. The extraction of a first-order term from a given polynomial fraction is carried out in terms of the evaluation of zi at time functions. In the application to linear time-varying systems, it is proved that the VIT transform of the system output is equal to the product of the VIT transform of the input and the VIT transform of the unit-pulse response function. For systems given by a time-varying moving average or an autoregressive model, the transform framework is used to determine the steady-state output response resulting from various signal inputs such as the step and cosine functions.

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