ARIMA model building and the time series analysis approach to forecasting

1983 ◽  
Vol 2 (1) ◽  
pp. 23-35 ◽  
Author(s):  
Paul Newbold
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Model Building ◽  
Arima Model ◽  
Analysis Approach ◽  
Series Analysis

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).  

scholarly journals Market Response, Competitive Behavior, and Time Series Analysis

1980 ◽  
Vol 17 (4) ◽  
pp. 470-485 ◽  
Author(s):  
Dominique M. Hanssens
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Model Building ◽  
Competitive Reaction ◽  
Marketing Model ◽  
Series Analysis ◽  
Marketing Variables ◽  
Major Airlines ◽  
Market Variables ◽  
The Relationship

The author's principal objective is to present a framework for market analysis which specifically models primary demand, competitive reaction, and feedback effects of the market variables. The approach is an extension of earlier work by Clarke and by Lambin, Naert, and Bultez on the relationship among the elasticities of the marketing variables. The author develops this framework and formulates an approach for empirical applications based on principles of time series analysis. In particular, Granger's well-known causality definition is used in conjunction with Box-Jenkins analysis to find the nonzero elements in the marketing model. These principles are applied empirically to the case of a city pair of the U.S. domestic air travel market, where three major airlines compete on the basis of flight scheduling and advertising. The analysis reveals that flight scheduling has a market-expansive or a competitive effect, depending on the competitor, and that advertising does not have a significant impact on performance. In addition, several patterns of competitive reactions are found. The author offers observations on the theoretical and empirical aspects of this approach to marketing model building.

Monitoring Human-Virtual Reality Interaction: A Time Series Analysis Approach

2000 ◽  
Vol 3 (3) ◽  
pp. 401-406 ◽  
Author(s):  
Robert Stoermer ◽  
Ralph Mager ◽  
Andreas Roessler ◽  
Franz Mueller-Spahn ◽  
Alex H. Bullinger
Keyword(s):  
Time Series ◽  
Virtual Reality ◽  
Time Series Analysis ◽  
Analysis Approach ◽  
Series Analysis

scholarly journals Novel time series analysis approach for prediction of dialysis in critically ill patients using echo-state networks

Critical Care ◽  
2009 ◽  
Vol 13 (Suppl 1) ◽  
pp. P256
Author(s):  
T Verplancke ◽  
S Van Looy ◽  
K Steurbaut ◽  
D Benoit ◽  
F De Turck ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Analysis Approach ◽  
Series Analysis ◽  
Echo State Networks

scholarly journals Time series analysis of dengue cases reporting to a tertiary care hospital

2019 ◽  
Vol 6 (5) ◽  
pp. 2200
Author(s):  
Ravindra S. Kembhavi ◽  
Saurabha U. S.
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Tertiary Care Hospital ◽  
Tertiary Care ◽  
Arima Model ◽  
Public Health Problem ◽  
Major Public Health Problem ◽  
Care Hospital ◽  
Series Analysis ◽  
The City

Background: Dengue fever is a major public health problem, the concern is high as the disease is closely related to climate change.Methods: This was a retrospective study, conducted for 1 year in a tertiary care hospital in the city of Mumbai. Data of Dengue cases and climate for the city of Mumbai between 2011 and 2015 were obtained. Data was analysed using SPSS- time series analysis and forecasting model.Results: 33% cases belonged to the 21-30 years, proportion of men affected were more than women. A seasonal distribution of cases was observed. A strong correlation was noted between the total number of cases reported and (a) mean monthly rainfall and (b) number of days of rainfall. ARIMA model was used for forecasting.Conclusions: The trend analysis along with forecasting model helps in being prepared for the year ahead. 

scholarly journals State debt assessment and forecasting: time series analysis

2021 ◽  
Vol 18 (1) ◽  
pp. 65-75
Author(s):  
Fedir Zhuravka ◽  
Hanna Filatova ◽  
Petr Šuleř ◽  
Tomasz Wołowiec
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Visual Analysis ◽  
Arima Model ◽  
Financial System ◽  
Debt Management ◽  
Series Data ◽  
Model Parameters ◽  
Negative Consequences ◽  
Series Analysis

One of the pressing problems in the modern development of the world financial system is an excessive increase in state debt, which has many negative consequences for the financial system of any country. At the same time, special attention should be paid to developing an effective state debt management system based on its forecast values. The paper is aimed at determining the level of persistence and forecasting future values of state debt in the short term using time series analysis, i.e., an ARIMA model. The study covers the time series of Ukraine’s state debt data for the period from December 2004 to November 2020. A visual analysis of the dynamics of state debt led to the conclusion about the unstable debt situation in Ukraine and a significant increase in debt over the past six years. Using the Hurst exponent, the paper provides the calculated value of the level of persistence in time series data. Based on the obtained indicator, a conclusion was made on the confirmation of expediency to use autoregressive models for predicting future dynamics of Ukraine’s state debt. Using the EViews software, the procedure for forecasting Ukraine’s state debt by utilizing the ARIMA model was illustrated, i.e., the series was tested for stationarity, the time series of monthly state debt data were converted to stationary, the model parameters were determined and, as a result, the most optimal specification of the ARIMA model was selected.

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