Predicting Time Series of Temperature in Nineveh Using The Conversion Function Models

2021 ◽  
Vol 11 (2) ◽  
pp. 572
Author(s):  
Noor Al-Huda Mahmood Thamer ◽  
Najlaa Saad Ibrahim Alsharabi
Keyword(s):  
Time Series ◽  
Conversion Function ◽  
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.

The LDE-Type Flare Occurrence (1969-1993)

1994 ◽  
Vol 144 ◽  
pp. 279-282
Author(s):  
A. Antalová
Keyword(s):  
Time Series ◽  
Fourier Analysis ◽  
Sunspot Cycle ◽  
List Type ◽  
Type Number ◽  
Solar Cycles ◽  
Type Iv ◽  
Long Duration ◽  
Cycle Maximum ◽  
Flare Index

AbstractThe occurrence of LDE-type flares in the last three cycles has been investigated. The Fourier analysis spectrum was calculated for the time series of the LDE-type flare occurrence during the 20-th, the 21-st and the rising part of the 22-nd cycle. LDE-type flares (Long Duration Events in SXR) are associated with the interplanetary protons (SEP and STIP as well), energized coronal archs and radio type IV emission. Generally, in all the cycles considered, LDE-type flares mainly originated during a 6-year interval of the respective cycle (2 years before and 4 years after the sunspot cycle maximum). The following significant periodicities were found:• in the 20-th cycle: 1.4, 2.1, 2.9, 4.0, 10.7 and 54.2 of month,• in the 21-st cycle: 1.2, 1.6, 2.8, 4.9, 7.8 and 44.5 of month,• in the 22-nd cycle, till March 1992: 1.4, 1.8, 2.4, 7.2, 8.7, 11.8 and 29.1 of month,• in all interval (1969-1992):a)the longer periodicities: 232.1, 121.1 (the dominant at 10.1 of year), 80.7, 61.9 and 25.6 of month,b)the shorter periodicities: 4.7, 5.0, 6.8, 7.9, 9.1, 15.8 and 20.4 of month.Fourier analysis of the LDE-type flare index (FI) yields significant peaks at 2.3 - 2.9 months and 4.2 - 4.9 months. These short periodicities correspond remarkably in the all three last solar cycles. The larger periodicities are different in respective cycles.

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