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 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 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.

Consistency for the least squares estimator in a transfer function model

1984 ◽  
Vol 21 (01) ◽  
pp. 88-97
Author(s):  
Victor Solo
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Least Squares ◽  
Time Series Model ◽  
Least Squares Estimator ◽  
Transfer Function Model ◽  
Function Model ◽  
Mild Conditions ◽  
Function Time

The consistency is developed under mild conditions for the least squares estimator of the parameters of a transfer function time series model.

scholarly journals Transfer Function Analysis: Modelling Residential Building Costs in New Zealand by Including the Influences of House Price and Work Volume

Buildings ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 152
Author(s):  
Linlin Zhao ◽  
Jasper Mbachu ◽  
Zhansheng Liu ◽  
Huirong Zhang
Keyword(s):  
Time Series ◽  
New Zealand ◽  
Transfer Function ◽  
Moving Average ◽  
Cost Data ◽  
Autoregressive Integrated Moving Average ◽  
Work Volume ◽  
Moving Average Models ◽  
Function Models ◽  
Transfer Function Models

An accurate cost estimate not only plays a key role in project feasibility studies but also in achieving a final successful outcome. Conventionally, estimating cost typically relies on the experience of professionals and cost data from previous projects. However, this process is complex and time-consuming, and it is challenging to ensure the accuracy of the estimates. In this study, the bivariate and multivariate transfer function models were adopted to estimate and forecast the building costs of two types of residential buildings in New Zealand: Low-rise buildings and high-rise buildings. The transfer function method takes advantage of the merits of univariate time series analysis and the power of explanatory variables. In the dynamic project conduction environment, simply including building cost data in the cost forecasting models is not valid for making predictions, because the change in demand must be considered. Thus, the time series of house prices and work volume were used to explain exogenous effects in the transfer function model. To demonstrate the effectiveness of transfer function models, this study compared the results generated by the transfer function models with autoregressive integrated moving average models. According to the forecasting performance of the models, the proposed approach achieved better results than autoregressive integrated moving average models. The proposed method can provide accurate cost estimates that can help stakeholders in project budget planning and management strategy making at the early stage of a project.

scholarly journals Modelling of Multi Input Transfer Function for Rainfall Forecasting in Batu City

CAUCHY ◽  
2017 ◽  
Vol 5 (1) ◽  
pp. 29
Author(s):  
Priska Arindya Purnama
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Transfer Function ◽  
Air Temperature ◽  
Multivariate Time Series ◽  
Time Series Model ◽  
Transfer Function Model ◽  
Air Humidity ◽  
Rainfall Forecasting ◽  
Function Model

The aim of this research is to model and forecast the rainfall in Batu City using multi input transfer function model based on air temperature, humidity, wind speed and cloud. Transfer function model is a multivariate time series model which consists of an output series (Y<sub>t</sub>) sequence expected to be effected by an input series (X<sub>t</sub>) and other inputs in a group called a noise series (N<sub>t</sub>). Multi input transfer function model obtained is (<em>b<sub>1</sub>,s<sub>1</sub>,r<sub>1</sub></em>) (<em>b<sub>2</sub>,s<sub>2</sub>,r<sub>2</sub></em>) (<em>b<sub>3</sub>,s<sub>3</sub>,r<sub>3</sub></em>) (<em>b<sub>4</sub>,s<sub>4</sub>,r<sub>4</sub></em>)(<em>p<sub>n</sub>,q<sub>n</sub></em>) = (0,0,0) (23,0,0) (1,2,0) (0,0,0) ([5,8],2) and shows that air temperature on t-day affects rainfall on t-day, rainfall on t-day is influenced by air humidity in the previous 23 days, rainfall on t-day is affected by wind speed in the previous day , and rainfall on day t is affected by clouds on day t. The results of rainfall forecasting in Batu City with multi input transfer function model can be said to be accurate, because it produces relatively small RMSE value. The value of RMSE data forecasting training is 7.7921 while forecasting data testing is 4.2184. Multi-input transfer function model is suitable for rainfall in Batu City.

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.

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