scholarly journals Application of ARIMA models on Export potential Indicator

2021 ◽  
Vol 8 (2) ◽  
pp. 1165-1180
Author(s):  
Amour Gbaguidi Amoussou ◽  
Aristide Medenou
Keyword(s):  
Mathematical Model ◽  
Econometric Model ◽  
Arima Model ◽  
Arima Models ◽  
Potential Indicator ◽  
Target Markets

The export potential indicator is designed for countries that aim to support established exports by increasing exports to new or existing target markets, and several studies are being managed using various mathematical model to predict the export values. Here, we propose an econometric model that could be useful to predict the export values. We performed the ARIMA model to evaluate the realized and unrealized export potentials of products. We therefore propose to carry out actions in favor of increasing the export potential.

scholarly journals Hybrid Model for Time Series of Complex Structure with ARIMA Components

Mathematics ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1122
Author(s):  
Oksana Mandrikova ◽  
Nadezhda Fetisova ◽  
Yuriy Polozov
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Hybrid Model ◽  
Complex Structure ◽  
Arima Model ◽  
Ionospheric Parameter ◽  
Arima Models ◽  
Complicated Structure ◽  
Threshold Functions ◽  
Anomalous Component

A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

scholarly journals Estimating COVID-19 cases in Makkah region of Saudi Arabia: Space-time ARIMA modeling

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0250149
Author(s):  
Fuad A. Awwad ◽  
Moataz A. Mohamoud ◽  
Mohamed R. Abonazel
Keyword(s):  
Saudi Arabia ◽  
Moving Average ◽  
Arima Model ◽  
Daily Basis ◽  
World Health ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
First Case ◽  
Novel Coronavirus ◽  
The Impact

The novel coronavirus COVID-19 is spreading across the globe. By 30 Sep 2020, the World Health Organization (WHO) announced that the number of cases worldwide had reached 34 million with more than one million deaths. The Kingdom of Saudi Arabia (KSA) registered the first case of COVID-19 on 2 Mar 2020. Since then, the number of infections has been increasing gradually on a daily basis. On 20 Sep 2020, the KSA reported 334,605 cases, with 319,154 recoveries and 4,768 deaths. The KSA has taken several measures to control the spread of COVID-19, especially during the Umrah and Hajj events of 1441, including stopping Umrah and performing this year’s Hajj in reduced numbers from within the Kingdom, and imposing a curfew on the cities of the Kingdom from 23 Mar to 28 May 2020. In this article, two statistical models were used to measure the impact of the curfew on the spread of COVID-19 in KSA. The two models are Autoregressive Integrated Moving Average (ARIMA) model and Spatial Time-Autoregressive Integrated Moving Average (STARIMA) model. We used the data obtained from 31 May to 11 October 2020 to assess the model of STARIMA for the COVID-19 confirmation cases in (Makkah, Jeddah, and Taif) in KSA. The results show that STARIMA models are more reliable in forecasting future epidemics of COVID-19 than ARIMA models. We demonstrated the preference of STARIMA models over ARIMA models during the period in which the curfew was lifted.

scholarly journals Growth, instability and forecast of marine products export from India

2016 ◽  
Vol 63 (4) ◽  
Author(s):  
Apu Das ◽  
Nalini Ranjan Kumar ◽  
Prathvi Rani
Keyword(s):  
Moving Average ◽  
Export Performance ◽  
Arima Model ◽  
South East Asia ◽  
Arima Models ◽  
Policy Makers ◽  
Increasing Trend ◽  
Marine Products ◽  
Auto Regressive ◽  
Instability Indices

This paper analysed growth and instability in export of marine products from India with an attempt to forecast the total export quantity of marine products from the country. The compound growth rates and instability indices of marine products export from India were estimated for major importing countries viz., Japan, USA, European Union, South-east Asia and Middle East; as more than 80% of the marine products export from India destines to these markets. The study revealed high compound growth rate and low instability in case of selected countries. The study also revealed that India’s marine products export concentrated mainly to those countries, which were falling in less desirable or least desirable category which has affected export performance of the country. Forecast of India’s marine products export was done by fitting univariate Auto Regressive Integrated Moving Average (ARIMA) models. ARIMA (1, 1, 0) was found suitable for modelling marine products export from India. The results of ARIMA model indicated increasing trend in export of Indian marine products. This calls for serious attention by policy makers to identify competitive and stable market destinations for marine products export which could help in harnessing the potential of marine products export from India.

scholarly journals Airport Trends Analytics Engine using the ARIMA Model

2018 ◽  
Vol 7 (3.12) ◽  
pp. 239
Author(s):  
Chitransh Rajesh ◽  
Yash Jain ◽  
J Jayapradha
Keyword(s):  
Data Analytics ◽  
Arima Model ◽  
Interactive Visualization ◽  
Database Management System ◽  
Data Sets ◽  
Arima Models ◽  
End User ◽  
Report Generation ◽  
Efficient Data ◽  
Visualization Of Data

Data Analytics is the process of analyzing unprocessed data to draw conclusions by studying and inspecting various patterns in the data. Several algorithms and conceptual methods are often followed to derive legit and accurate results. Efficient data handling is important for interactive visualization of data sets. Considering recent researches and analytical theories on column-oriented Database Management System, we are developing a new data engine using R and Tableau to predict airport trends. The engine uses Univariate datasets (Example, Perth Airport Passenger Movement Dataset, and Newark Airport Cargo Stats Dataset) to analyze and predict accurate trends. Data analyzing and prediction is done with the implementation of Time Series Analysis and respective ARIMA Models for respective modules. Development of modules is done using RStudio whereas Tableau is used for interactive visualization and end-user report generation. The Airport Trends Analytics Engine is an integral part of R and Tableau 10.4 and is optimized for use on desktop and server environments.  

scholarly journals Forecasting of boro rice production in Bangladesh: An ARIMA approach

1970 ◽  
Vol 8 (1) ◽  
pp. 103-112 ◽  
Author(s):  
NMF Rahman
Keyword(s):  
Time Series ◽  
Arima Model ◽  
Rice Production ◽  
Arima Models ◽  
Short Term ◽  
Production Uncertainty ◽  
Future Production ◽  
Short Run ◽  
The Government ◽  
Boro Rice

The study was undertaken to examine the best fitted ARIMA model that could be used to make efficient forecast boro rice production in Bangladesh from 2008-09 to 2012-13. It appeared from the study that local, modern and total boro time series are 1st order homogenous stationary. It is found from the study that the ARIMA (0,1,0) ARIMA (0,1,3) and ARIMA (0,1,2) are the best for local, modern and total boro rice production respectively. It is observed from the analysis that short term forecasts are more efficient for ARIMA models. The production uncertainty of boro rice can be minimizing if production can be forecasted well and necessary steps can be taken against losses. The government and producer as well use ARIMA methods to forecast future production more accurately in the short run. Keywords: Production; ARIMA model; Forecasting. DOI: 10.3329/jbau.v8i1.6406J. Bangladesh Agril. Univ. 8(1): 103-112, 2010

scholarly journals Nowcasting GDP of Bosnia and Herzegovina: A Comparison of Forecast Accuracy Models

2020 ◽  
Vol 15 (2) ◽  
pp. 1-14
Author(s):  
Ademir Abdić ◽  
Emina Resić ◽  
Adem Abdić ◽  
Adnan Rovčanin
Keyword(s):  
Bosnia And Herzegovina ◽  
Econometric Model ◽  
Forecast Accuracy ◽  
Arima Models ◽  
Retail Sale ◽  
Short Term ◽  
Final Choice ◽  
Bridge Model ◽  
Time Periods ◽  
Bridge Models

AbstractThe paper explores the possibilities of creating an econometric model for making short-term forecasts of the Gross Domestic Product of Bosnia and Herzegovina (GDP of B&H). Its aim is to determine the most representative and most efficient model for forecasting the quarterly GDP of B&H. This is the first paper that simultaneously compares ARIMA models, bridge models and factor models in three different time periods. All variables are available for the period of 2006q1-2016q4. The final choice of the model for forecasting the quarterly GDP of B&H was selected on the basis of a comparative analysis of the predictive efficiency of the analysed models. Based on the obtained results, the most efficient model for forecasting quarterly GDP of B&H is the bridge model, which includes four variables as regressor: Retail sale of other goods, Total loans, Manufacturing and Manufacture of food products.

scholarly journals Using Autoregressive Integrated Moving Average (ARIMA) Technique to Forecast the Production of Kharif Cereals in Odisha (India)

2020 ◽  
pp. 104-113
Author(s):  
Abhiram Dash ◽  
A. Mangaraju ◽  
Pradeep Mishra ◽  
H. Nayak
Keyword(s):  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Percentage Error ◽  
Arima Models ◽  
Training Set ◽  
Autoregressive Integrated Moving Average ◽  
Future Production ◽  
Best Fit ◽  
Testing Set

Cereals are the most important kharif season crop in Odisha. The present study was carried out to forecast the production of kharif cereals in Odisha by using the forecast values of area and yield of kharif cereals obtained from the selected best fit Autoregressive Integrated Moving Average (ARIMA) model. The data from 1970-71 to 2010-11 are considered as training set data and used for model building and from 2011-12 to 2015-16 are considered as testing set data and used for cross-validation of the selected model on the basis of the absolute percentage error. The ARIMA models are fitted to the stationary data which may be the original data or the differenced data. The different ARIMA models are evaluated on the basis of Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) at various lags. The possible ARIMA models are selected on the basis of significant coefficient of autoregressive and moving average components by using the training set data. The best fitted models are then selected on the basis of residual diagnostics test and model fit statistics. The ARIMA model found to be best fitted for area under kharif cereals and yield of kharif cereals are ARIMA (1,1,0) without constant and ARIMA (0,1,2) without constant respectively which are successfully cross-validated with the testing set data. The respective best fit ARIMA model has been used to forecast the area and yield of kharif cereals for the years 2016-17, 2017-18 and 2018-19. The forecast values of area shows a decrease, whereas, the forecast values of yield shows an increase. The decrease in area might have been the result of limited availability of area for cereals due to shifting towards non-food grain crops. The forecast values of production of kharif cereals obtained from the forecast values of area and yield of kharif cereals shows an increase which is due to the increase in forecast values of yield. Since there is limited scope for area expansion, the future production of kharif cereals can only be increased by increasing the yield to achieve the goal of food security for the growing population.

scholarly journals Time series data analysis and ARIMA modeling to forecast the short-term trajectory of the acceleration of fatalities in Brazil caused by the corona virus (COVID-19)

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11748
Author(s):  
Akini James ◽  
Vrijesh Tripathi
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Explanatory Variable ◽  
Arima Model ◽  
Series Data ◽  
Arima Models ◽  
Composite Function ◽  
Short Run ◽  
Lag Model ◽  
Time Series Data Analysis

Objective This paper incorporates the concept of acceleration to fatalities caused by the coronavirus in Brazil from time series data beginning on 17th March 2020 (the day of the first death) to 3rd February 2021 to explain the trajectory of the fatalities for the next six months using confirmed infections as the explanatory variable. Methods Acceleration of the cases of confirmed infection and fatalities were calculated by using the concept of derivatives. Acceleration of fatality function was then determined from multivariate linear function and calculus chain rule for composite function with confirmed infections as an explanatory variable. Different ARIMA models were fitted for each acceleration of fatality function: the de-seasonalized Auto ARIMA Model, the adjusted lag model, and the auto ARIMA model with seasonality. The ARIMA models were validated. The most realistic models were selected for each function for forecasting. Finally, the short run six-month forecast was conducted on the trajectory of the acceleration of fatalities for all the selected best ARIMA models. Results It was found that the best ARIMA model for the acceleration functions were the seasonalized models. All functions suggest a general decrease in fatalities and the pace at which this change occurs will eventually slow down over the next six months. Conclusion The decreasing fatalities over the next six-month period takes into consideration the direct impact of the confirmed infections. There is an early increase in acceleration for the forecast period, which suggests an increase in daily fatalities. The acceleration eventually reduces over the six-month period which shows that fatalities will eventually decrease. This gives health officials an idea on how the fatalities will be affected in the future as the trajectory of confirmed COVID-19 infections change.

scholarly journals Forecasting of saline intrusion in Ham Luong river, Ben Tre province (Southern Vietnam) using Box-Jenkins ARIMA models

2020 ◽  
Vol 23 (1) ◽  
pp. 446-453
Author(s):  
Thai Thanh Tran ◽  
Luong Duc Thien ◽  
Ngo Xuan Quang ◽  
Lam Van Tan
Keyword(s):  
Economic Development ◽  
Moving Average ◽  
Arima Model ◽  
Mekong River ◽  
Saline Intrusion ◽  
Arima Models ◽  
Southern Vietnam ◽  
Forecasting Methods ◽  
Auto Regressive ◽  
Local Residents

Introduction: Ham Luong River is a branch of Mekong River located in Ben Tre Province, which has played a crucial role in supporting livelihoods of local residents and the province's economic development. However, the saline intrusion has been expanding in Ham Luong River, which seriously affects the productive agriculture, aquaculture, and further causes tremendous difficulties for local people's lives. Thus, it is crucial to have research for forecast the saline intrusion in Ham Luong River. Our aim was to develop mathematical models in order to forecast the saline intrusion in Ham Luong River, Ben Tre Province. Methods: The Auto regressive integrated moving average (ARIMA) model was built to forecast the weekly saline intrusion in Ham Luong River, which has been obtained from Ben Tre Province's Hydro-Meteorological Forecasting Center over eight years (from 2012 to 2019). Results: The saline concentration increased from January to March and then decreased from April to June. The highest salinity occurred in February and March while the lowest salinity was observed in early June. Moreover, the ARIMA technique provided an adequate predictive model for a forecast of the saline intrusion in An Thuan, Son Doc, and An Hiep station. However, the ARIMA model in My Hoa and Vam Mon might be improved upon by other forecasting methods. Conclusion: Our study suggested that the nonseasonal/seasonal ARIMA is an easy-to-use modeling tool for a quick forecast of the saline intrusion.

scholarly journals Forecasting tourist arrivals in South Africa

2010 ◽  
Vol 10 (1) ◽  
Author(s):  
Andrea Saayman ◽  
Melville Saayman
Keyword(s):  
South Africa ◽  
Emerging Market ◽  
Arima Model ◽  
The United States ◽  
Interesting Case ◽  
Percentage Error ◽  
Business Success ◽  
Arima Models ◽  
Time Series Modelling ◽  
Seasonal Arima Models

Purpose: The aim of this paper is to model and forecast tourism to South Africa from the country's main intercontinental tourism markets. These include Great Britain, Germany, the Netherlands, the United States of America and France. Problem investigated: Tourism to South Africa has grown substantially since the first democratic elections in 1994. It is currently the third largest industry in the country and a vital source of foreign exchange earnings. Tourist arrivals continue to grow annually, and have shown some resilience to a number of emerging market crises, including the terrorist attacks in the USA. Business success, marketing decisions, government's investment policy as well as macroeconomic policy are influenced by the accuracy of tourism forecasts, since the tourism product comprises a number of services that cannot be accumulated. Accurate forecasts of tourism demand are paramount to ensure the availability of such services when demanded. In addition, the seasonal nature of tourism leads to a pattern of excess capacity followed by shortage in capacity. Method: Since univariate time series modelling has proved to be a very successful method for forecasting tourist arrivals, it is also the method employed in this paper. The naïve model is tested against a standard ARIMA model, as well as the Holt-Winters exponential smoothing and seasonal-non-seasonal ARIMA models. Forecasting accuracy is assessed using the mean absolute percentage error, root mean square error and Theill's U of the various models. Monthly tourist arrivals from 1994 to 2006 are used in the analysis, and arrivals are forecasted for 2007. Findings: The results show that seasonal ARIMA models deliver the most accurate predictions of arrivals over three time horizons, namely three months, six months and 12 months. Value: This paper is the first tourist arrivals forecast using South African data for the country as a whole, and therefore it forms an interesting case study as a long haul and growing tourist destination. Conclusion: The univariate forecasts provide fairly accurate forecasts of tourist arrivals in South Africa, especially over the short run. As such, it is understandable why it remains a popular approach to forecast tourist arrivals. However, this method does not make provision for assessing the influence of external events and therefore its policy application is limited.

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