scholarly journals Prediction Model of Firefighting Supplies Market

2021 ◽  
Vol 35 (4) ◽  
pp. 91-96
Author(s):  
Inseon Park ◽  
Muheon Jeong
Keyword(s):  
Transfer Function ◽  
Construction Industry ◽  
Structural Characteristics ◽  
Arima Model ◽  
Statistical Prediction ◽  
Transfer Function Model ◽  
Short Term ◽  
Function Model ◽  
Construction Market ◽  
The Relationship

The market size of the firefighting supplies industry is can be considered a derivative demand of the construction industry, and its patterns differ seasonally. In this study, the seasonal ARIMA model and the transfer function model were established, and their predictive performances were verified. It was statistically confirmed that the firefighting supplies market has a lagging character in the construction industry. Although the seasonal ARIMA model, a representative time-series model, is a suitable predictive model, the transfer function model linked to the construction market is more useful in the short-term forecast period. This study is the first statistical prediction study of the firefighting supplies market in Korea. Based on the structural characteristics of the firefighting supplies industry, the relationship between the building permit and firefighting supplies production should be established using the transfer function model, the transfer function model is more useful in the decision-making process of firefighting supplies policies and the manufacturer’s marketing.

scholarly journals PERAMALAN JUMLAH KUNJUNGAN WISATAWAN MANCANEGARA YANG BEKUNJUNG KE BALI MENGGUNAKAN FUNGSI TRANSFER

2016 ◽  
Vol 5 (4) ◽  
pp. 139
Author(s):  
I KETUT PUTRA ADNYANA ◽  
I WAYAN SUMARJAYA ◽  
I KOMANG GDE SUKARSA
Keyword(s):  
Exchange Rate ◽  
Transfer Function ◽  
Multivariate Time Series ◽  
Arima Model ◽  
Information Criterion ◽  
Percentage Error ◽  
Transfer Function Model ◽  
Function Model ◽  
Diagnostic Checking ◽  
The Exchange Rate

The aim of this research is to model and forecast the number of tourist arrivals to Bali using transfer function model based on exchange rate USD to IDR from January 2009 to December 2015. Transfer function model is a multivariate time series model which can be used to identify the effect of the exchange rate to the number of tourist arrivals to Bali. The first stage in transfer function modeling is identification of ARIMA model in exchange rate USD to IDR variable. The best ARIMA model is chosen based on the smallest Akaike information criterion (AIC). The next stage are as follows identification of transfer function model, estimation of transfer function model, and diagnostic checking for transfer function model. The estimated transfer function model suggests that the number of tourist arrivals to Bali is affected by the exchange rate of the previous eight months. The mean absolute percentage error (MAPE) is equal of the forecasting model to 9,62%.

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 Analisis Pengaruh Kurs USD terhadap Jakarta Islamic Index dengan Menggunakan Model Fungsi Transfer

2018 ◽  
Vol 2 (2) ◽  
pp. 66-72
Author(s):  
Pika Silvianti ◽  
Nur Laela Fitriani
Keyword(s):  
Exchange Rate ◽  
Transfer Function ◽  
Explanatory Variable ◽  
Arima Model ◽  
Diagnostic Testing ◽  
Forecasting Model ◽  
Transfer Function Model ◽  
Function Model ◽  
Model Diagnostic ◽  
The Exchange Rate

The transfer function model is a time series forecasting model that combines several characteristics ofthe ARIMA model one variable with several characteristics of regression analysis. This model is used to determine the effect of an explanatory variable (input series) on the response variable (output series). This study uses a transfer function model to analyze the effect of the exchange rate on Jakarta Islamic Index. The transfer function model is structured through several stages, starting from modelidentification, estimation of the transfer function model, and model diagnostic testing. Based on the transfer function model, Jakarta Islamic Index was influenced by Jakarta Islamic Index in one and two days earlier and the exchange rate in the same period and one to two days earlier. The forecasting MAPE value of 0.6529% shows that the transfer function model obtained is good enough in forecasting.

On the Efficiency Between Forecasting Models—A Study in the Indian Context

1987 ◽  
Vol 36 (1-2) ◽  
pp. 19-28
Author(s):  
Lakshmikanta Datta
Keyword(s):  
Transfer Function ◽  
Moving Average ◽  
Arima Model ◽  
Consumer Price Index ◽  
Transfer Function Model ◽  
Industrial Workers ◽  
Economic Time Series ◽  
Function Model ◽  
Forecasting Models ◽  
Economic Time

In this paper, we have investigated the relative performances of two types of forecasting models, namely univariate autoregressive integrated moving average (ARIMA) model and transfer function model, with the help of two Indian economic time series viz. (i) Money Supply (M3 ) and (ii) Consumer Price Index Numbers for Industrial Workers. Our emperical results show that the efficiency of transfer function model is substantially superior to that of the univariate model.

The State Monitoring Method of Electronic Voltage Transformer Based on L-M Algorithm

2019 ◽  
Vol 23 (3) ◽  
pp. 385-389
Author(s):  
Han Lian ◽  
Keyword(s):  
Transfer Function ◽  
Pressure Ratio ◽  
The State ◽  
Transfer Function Model ◽  
State Monitoring ◽  
Voltage Transformer ◽  
Function Model ◽  
Frequency Condition ◽  
Monitoring Method ◽  
The Relationship

In the traditional method of monitoring the state of electronic voltage transformer, there are problems of large monitoring error and weak robustness. Therefore, a new state monitoring method of electronic voltage transformer based on L-M algorithm is proposed. The relationship between input voltage and output voltage of capacitor voltage divider in electronic voltage transformer is obtained by using Laplasse transform. The transfer function model of electronic voltage transformer is constructed based on the relationship result and L-M algorithm. The transfer function model is used to analyze the frequency characteristics of the electronic voltage transformer and the range of normal measurement frequency, and then the partial pressure ratio of the electronic voltage transformer under the high frequency condition is derived. On this basis, by calculating the over voltage amplitude on the two sides of acquisition card in the electronic voltage transformer, the capacitance value between the two adjacent coaxial cylindrical cylinders of the capacitance divider in the electronic voltage transformer is obtained, thus the monitoring of the state of the electronic voltage transformer is completed. The experimental results show that the proposed method has low detection error and strong robustness, and can effectively improve the reliability of electronic voltage transformer.

Model Fungsi Transfer Input Ganda untuk Pemodelan Jakarta Islamic Index

2019 ◽  
Vol 7 (3) ◽  
Author(s):  
Nur Laela Fitriani ◽  
Pika Silvianti ◽  
Rahma Anisa
Keyword(s):  
Exchange Rate ◽  
Transfer Function ◽  
Multivariate Time Series ◽  
Model Identification ◽  
Transfer Function Model ◽  
Function Model ◽  
Jones Index ◽  
Multiple Input ◽  
Identification Stage ◽  
Stage Estimation

Transfer function model with multiple input is a multivariate time series forecasting model that combines several characteristics of ARIMA models by utilizing some regression analysis properties. This model is used to determine the effect of output series towards input series so that the model can be used to analyze the factors that affect the Jakarta Islamic Index (JII). The USD exchange rate against rupiah and Dow Jones Index (DJI) were used as input series. The transfer function model was constructed through several stages: model identification stage, estimation of transfer function model, and model diagnostic test. Based on the transfer function model, the JII was influenced by JII at the period of one and two days before. JII was also affected by the USD exchange rate against rupiah at the same period and at one and two days before. In addition, the JII was influenced by DJI at the same period and also at period of one until five days ago. The Mean Absolute Prencentage Error (MAPE) value of forecasting result was 0.70% and the correlation between actual and forecast data was 0.77. This shows that the model was well performed for forecasting JII.

scholarly journals Administrative Costs and Tariff Rates in the Presence of Customs Evasion: Evidence from Ecuador

Economies ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 21
Author(s):  
Jazmín González Aguirre ◽  
Alberto Del Villar
Keyword(s):  
International Trade ◽  
Transfer Function ◽  
Gross Domestic Product ◽  
Foreign Trade ◽  
Transfer Function Model ◽  
Administrative Cost ◽  
Function Model ◽  
Administrative Costs ◽  
Tariff Rates ◽  
Budgetary Expenditure

This paper seeks to assess the effectiveness of customs policies in increasing the resources devoted to controlling and inspection. Specifically, it seeks to analyze whether an increase in the administrative cost of collecting taxes on foreign trade in Ecuador contributes to reducing customs fraud. To this end, we identify and estimate a transfer function model (ARIMAX), considering information on foreign trade such as official international trade statistics report and tariff rates, as well as the execution of budgetary expenditure and Ecuador’s gross domestic product (GDP). The period under study includes quarterly series from 2006 to 2018. The results obtained by the model indicate that allocating greater material and budgetary resources to combat customs fraud does not always achieve the objective of reducing customs evasion.

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