Time series analysis of covariance based on linear transfer function models

2018 ◽  
Vol 22 (1) ◽  
pp. 1-16
Author(s):  
M. Azimmohseni ◽  
M. Khalafi ◽  
M. Kordkatuli
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Time Series Analysis ◽  
Analysis Of Covariance ◽  
Series Analysis ◽  
Linear Transfer Function ◽  
Function Models ◽  
Transfer Function Models

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.

Time Series Models of the Maine Lobster Fishery: The Effect of Temperature

1988 ◽  
Vol 45 (7) ◽  
pp. 1145-1153 ◽  
Author(s):  
Michael J. Fogarty
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Water Temperature ◽  
Fishing Effort ◽  
Effect Of Temperature ◽  
Time Series Models ◽  
Catch Per Unit Effort ◽  
Lobster Fishery ◽  
Function Models ◽  
Transfer Function Models

I used Box–Jenkins transfer function models to analyze the relationship between water temperature and Maine lobster catch and catch-per-unit-effort (CPUE). I first modelled catch and CPUE with univariate autoregressive – integrated moving average (ARIMA) models to provide a basis for comparison with transfer function models. Time series models were constructed for annual Maine lobster landings during two periods: 1928–85 and 1945–85; catches during the latter period were assumed to be less dependent on changes in fishing effort. I also modelled annual CPUE for the period 1930–85 and monthly landings for 1968–85. Landings and CPUE for 1986 were held in reserve to check forecast estimates. An immediate temperature effect (lag 0–1 yr) was demonstrated for each annual series. This result is consistent with known aspects of lobster biology; activity levels and hence vulnerability to capture increase with water temperature. In addition, the probability of molting increases with increasing water temperature, affecting the short-term supply of legal-sized lobsters. A significant effect of temperature at a 6-yr lag was also indicated, but only for the 1945–85 catch series. Delayed effects of this type may indicate environmental influences on natality or survival during early life history stages. Time series models for the Maine lobster fishery provided forecasts for 1986 catch and CPUE which differed by no more than 4% of the actual 1986 levels.

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%.

Modeling and Forecasting Primary Production Rates Using Box–Jenkins Transfer Function Models

1987 ◽  
Vol 44 (5) ◽  
pp. 1045-1052 ◽  
Author(s):  
Aimee Keller
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Primary Production ◽  
Residual Variance ◽  
Production Rates ◽  
Modeling And Forecasting ◽  
Input Variables ◽  
Function Models ◽  
Nutrient Addition Experiment ◽  
Transfer Function Models

Box–Jenkins transfer function models were developed for time series of integrated hourly primary production rates. A 28-mo record of 56 biweekly measurements collected from seven mesocosms during a nutrient addition experiment was analyzed. Incorporation of two input variables (phytoplankton biomass and hourly light) significantly improved the fit of the models. When compared with standard regression models, the time series models all had reduced residual variance. The forecasting ability of the final fitted model for a control system was demonstrated with independent data from the two replicate control mesocosms.

scholarly journals PERAMALAN JUMLAH PENDERITA DEMAMBER DARAH DENGUE DI KABUPATEN JOMBANG JAWA TIMUR DENGAN PENDEKATAN FUNGSI TRANSFER SINGLE INPUT

2018 ◽  
Vol 15 (2) ◽  
pp. 10
Author(s):  
Sediono Sediono
Keyword(s):  
Time Series ◽  
Transfer Function ◽  
Time Series Analysis ◽  
Dengue Fever ◽  
Stationary Time Series ◽  
Transfer Function Model ◽  
Function Model ◽  
Series Analysis ◽  
Future Data ◽  
Single Input

AbstractForecasting  is an important things in time series analysis, because by obtaining a convenient model that is statictically appropriate. Clearly, that can be used to predict the structure of future data form. Transfer function is one of mathematical model  in time series analysis, that can be used to forecasting time index data both univariate and multivariate. Transfer function describes the predictive value  of the output series (Yt) based on the value of one or more input series(Xt). The single input transfer function model is a transfer function model that uses one variable as input series (Xt), where each series of both input series and output series must be a stationary time series model, both stationary in the mean and stationary in variant. One of the used transfer function is to govern a model and forecasting of the number of cases dengue fever (Yt) in Kabupaten Jombang, East Java, where the input variable based on data of rainfall (Xt). From the result of this study was obtained that model of transfer function has a equation Y𝑡 = 0,0542X𝑡+  (1 − 0,7309𝐵)(1 + 0,6568𝐵12) with parameter ωo = 0.0542, ∅1 = 0.7309 and  Φ12 = -0.6568. From the model, it can be interpreted that the number of dengue sufferers for a particular month was influenced by the rainfall on those month and the months before. According to the model of the transfer function, it can be used to forecast the number of sufferers of dengue fever in Kabupaten Jombang  for period next 20 months. After compared between data of forecasting and actual data, there exists equally  trend, namely 15 months of 20 month that are forecasted, such that  it can be explain that majority 75% of the results of forecasting in this study are valid. Keywords: forecasting , single input transfer function, stationer point, Dengue fever  Abstrak Peramalan adalah sesuatu hal yang penting dalam analisis runtun waktu, karena dengan diperolehnya sebuah model yang tepat secara statistik, jelas hal tersebut dapat digunakan untuk memprediksi struktur pola data yang akan datang. Fungsi transfer merupakan salah satu model matematis dalam analisis runtun waktu  yang dapat digunakan untuk  peramalan data indekswaktu baik univariat maupun multivariat. Fungsi transfer menggambarkan nilai prediksi  dari  output series (Yt) berdasarkan nilai satu atau lebih input series (Xt). Model fungsi transfer  single input  adalah model fungsi transfer yang menggunakan satu variabel sebagai input series (Xt), dimana masing-masing series baik input series maupun output series keduanya harus sama-sama merupakan model runtun waktu yang stasioner, baik stasioner dalam mean maupun stasioner dalam varian. Salah satu penggunaan  model fungsi transfer ini adalah untuk pembuatan model dan peramalan jumlah kasus demam berdarah dengue (Yt)  di Kabupaten Jombang Jawa Timur, dengan variabel inputnya berdasarkan data curah hujan (Xt). Dari hasil penelitian diperoleh  model fungsi transfer yang memiliki persamaan  Y𝑡 = 0,0542X𝑡 +  (1 − 0,7309𝐵)(1 + 0,6568𝐵12) 𝑎𝑡 , dengan parameter ωo = 0,0542, ∅1 = 0,7309, dan Φ12 = -0,6568. Dari model tersebut dapat diinterpretasikan bahwa jumlah penderita demam berdarah dengue pada suatu bulan dipengaruhi curah hujan pada bulan itu, dan dipengaruhi oleh beberapa gangguan pada bulan-bulan sebelumnya. Selanjutnya berdasarkan model fungsi transfer tersebut dapat digunakan untuk peramalan jumlah penderita demam berdarah dengue di Kabupaten Jombang untuk periode 20 bulan kedepan. Setelah dilakukan perbandingan antara data hasil peramalan dengan data aktual, terdapat kesamaan trend yaitu sejumlah 15 bulan dari 20 bulan yang diramalkan, sehingga dapat dijelaskan bahwa sebagian besar yaitu 75% dari hasil peramalan  dalam penelitian ini adalah valid. Kata Kunci : Peramalan, Fungsi transfer single input, stasioner, Demam Berdarah Dengue. 

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