scholarly journals Proposal for a univariate time series analysis method based on Taguchi’s T - method

2019 ◽  
Vol 5 (1) ◽  
pp. 1-10
Author(s):  
Shushiro Matsui ◽  
Yasushi Nagata
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Analysis Method ◽  
Series Analysis ◽  
Univariate Time Series

scholarly journals Forecasting of Economic Indicators (Production, Consumption, Population) of Wheat Crop (A Case Study)

2021 ◽  
Vol 8 (6) ◽  
pp. 38-46
Author(s):  
Mohammad Karim Ahmadzai
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Wheat Crop ◽  
Series Analysis ◽  
The People ◽  
Self Sufficiency ◽  
Univariate Time Series ◽  
Short Run

Wheat is the most important food crop in Afghanistan, whether consumed by the bulk of the people or used in various sectors. The problem is that Afghanistan has a significant shortfall of wheat between domestic production and consumption. Thus, the present study looks at the issue of meeting self-sufficiency for the whole population due to wheat shortages. To do so, we employ time series analysis, which can produce a highly exact short-run prediction for a significant quantity of data on the variables in question. The ARIMA models are versatile and widely utilised in univariate time series analysis. The ARIMA model combines three processes: I the auto-regressive (AR) process, (ii) the differencing process, and (iii) the moving average (MA) process. These processes are referred to as primary univariate time series models in statistical literature and are widely employed in various applications. Where predicting future wheat requirements is one of the most important tools that decision-makers may use to assess wheat requirements and then design measures to close the gap between supply and consumption. The present study seeks to forecast Production, Consumption, and Population for the period 2002-2017 and estimate the values of these variables between 2002 and 2017. (2018-2030).  

Recent Mortality Trends in the Spanish Population

2002 ◽  
Vol 8 (4) ◽  
pp. 757-786 ◽  
Author(s):  
A. Felipe ◽  
M. Guillen ◽  
A. M. Perez-Marin
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Spanish Population ◽  
Mortality Trends ◽  
Calendar Time ◽  
Series Analysis ◽  
Univariate Time Series ◽  
Using Data ◽  
The Way

ABSTRACTOur research deals with the way that calendar time affects mortality patterns in the Spanish population, and how this information can be used to elaborate predictions. A description of the observed mortality evolution has been worked out using data from 1975 to 1993. We have used Heligman-Pollard Law number two to model the evolution of Spanish mortality over the period and using univariate time series analysis, we have obtained a prognosis for years 1994 to 2010.

scholarly journals Prediction Methods for Routine Maintenance Costs of a Reinforced Concrete Beam Bridge Based on Panel Data

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaoli Shi ◽  
Bingbing Zhao ◽  
Yuling Yao ◽  
Feng Wang
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Concrete Beam ◽  
Reinforced Concrete Beam ◽  
Maintenance Cost ◽  
Analysis Method ◽  
Series Analysis ◽  
The Cost ◽  
Beam Bridge ◽  
Physical Engineering

In order to make informed decisions on routine maintenance of bridges of expressways, the hierarchical regression analysis method was used to quantify factors influencing routine maintenance cost. Two calculation models for routine maintenance cost based on linear regression and time-series analysis were proposed. The results indicate that the logarithm of the historical routine maintenance cost is the dependent variable and the bridge age is the independent variable. The linear regression analysis was used to obtain a cost prediction model for routine maintenance of a beam bridge, which was combined with the quantity and price, and verified by a physical engineering example. In order to cope with the cost changes and future demands brought about by the emergence of new maintenance technologies, the time-series analysis method was used to obtain a model to predict the engineering quantities for the routine maintenance of a bridge based on standardized minor repair engineering quantities. Taking into account the actual cost of the minor repair project as well as the time-series analysis’ sample size demands, the annual engineering quantity was randomly decomposed into four quarterly data quantities, and the time-series analysis result was verified by physical engineering. These results can improve the calculation accuracy of the routine maintenance costs of reinforced concrete beam bridges. Furthermore, it can have a certain application value for improving the cost measurement module of bridge maintenance management systems.

ANALYSIS OF TIME SERIES WITH WAVELETS

2007 ◽  
Vol 05 (02) ◽  
pp. 241-256 ◽  
Author(s):  
ARMANDO CIANCIO
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Financial Time Series ◽  
Wavelet Coefficients ◽  
Analysis Method ◽  
Short Term ◽  
Financial Time ◽  
Local Characteristics ◽  
Series Analysis ◽  
Analysis Of Time Series

A financial time series analysis method based on the theory of wavelets is proposed. It is based on the transformation of data of the series in the corresponding wavelet coefficients and in the analysis of the latter, which represent the local characteristics of the series better. In particular, an algorithm for short term previsions is defined.

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