scholarly journals Predicting Economic Performance of Bangladesh using Autoregressive Integrated Moving Average (ARIMA) model.

2021 ◽  
pp. 129-148
Author(s):  
Raad Mozib Lalon ◽  
Nusrat Jahan
Keyword(s):  
Autocorrelation Function ◽  
Economic Performance ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Arima Model ◽  
Unit Root Test ◽  
Information Criterion ◽  
Gdp Growth ◽  
Autoregressive Integrated Moving Average ◽  
Q Model

This paper attempts to forecast the economic performance of Bangladesh measured with annual GDP data using an Autoregressive Integrated Moving Average (ARIMA) Model followed by test of goodness of fit using AIC (Akaike Information Criterion) and BIC (Bayesian Information Criterion) index value among six ARIMA models along with several diagnostic tests such as plotting ACF (Autocorrelation Function), PACF (Partial Autocorrelation Function) and performing Unit Root Test of the Residuals estimated by the selected forecasting ARIMA model. We have found the appropriate ARIMA (1,0,1) model useful in predicting the GDP growth of Bangladesh for next couple of years adopting Box-Jenkins approach to construct the ARIMA (p,r,q) model using the GDP data of Bangladesh provided in the World Bank Data stream from 1961 to 2019. JEL classification numbers: B22, B23, C53. Keywords: GDP growth, ACF, PACF, Stationary, ARIMA (p,r,q) model, Forecasting.

Water Use Trend at Universiti Teknologi Malaysia: Application of ARIMA Model

2012 ◽  
Author(s):  
Ayob Katimon ◽  
Amat Sairin Demun
Keyword(s):  
Time Series ◽  
Water Use ◽  
Autocorrelation Function ◽  
Water Consumption ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
University Campus ◽  
Autoregressive Integrated Moving Average ◽  
Partial Autocorrelation Function

Kertas kerja ini menerangkan aplikasi kaedah permodelan (ARIMA) bagi mewakili perilaku penggunaan air di kampus Universiti Teknologi Malaysia. Menggunakan fungsi–fungsi ACF, PACF dan AIC, siri masa penggunaan air bulanan di kampus UTM boleh dinyatakan dalam model ARIMA (2,0,0). Anggaran parameter model ø1 dan ø2 ialah 0.2747 dan 0.4194. Keadaan tersebut menggambarkan bahawa penggunaan air pada bulan semasa tidak semestinya dipengaruhi dengan tepat oleh kadar penggunaan air pada bulan sebelumnya. Analisis juga menunjukkan model ARIMA (2,0,0) boleh diguna sebagai model ramalan guna air di kampus universiti. Kata kunci: Guna air, kampus universiti, siri masa, model ARIMA The paper describes the application of autoregressive integrated moving average (ARIMA) model to represent water use behaviour at Universiti Teknologi Malaysia (UTM) campus. Using autocorrelation function (ACF), partial autocorrelation function (PACF), and Akaike’s Information Criterion (AIC), monthly campus water use series can be best presented using ARIMA (2,0,0) model. The estimated parameter of the model ø1 and ø2 are 0.2747 and 0.4194 respectively. This implies that water consumption in UTM campus at the present month is not necessarily influenced by water consumption of immediate previous month. Analysis shows that ARIMA (2,0,0) model provides a reasonable forecasting tool for campus water use. Key words: Water use, university campus, time series, ARIMA model

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.

A Stochastic Model to Analyze and Predict Transmission Dynamics of Tuberculosis in Ede Kingdom of Osun State

2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Kehinde Adekunle Bashiru
Keyword(s):  
Process Model ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Arima Model ◽  
Probability Generating Function ◽  
Information Criteria ◽  
Data Series ◽  
Suitable Model ◽  
Birth Process ◽  
Autoregressive Integrated Moving Average

In this study the stochastic process model for estimating the incidence of tuberculosis (TB) infection in Ede kingdom (Ede North and Ede South Local Government Areas) of Osun State was carried out. The probability generating function approach was used to solve the associated birth process model to obtain the estimate of TB incidence. Also time series analysis was carried out using JMulti software to predict future incidence rate of the disease in the study area. Based on Autoregressive Integrated Moving Average (ARIMA) model, the autocorrelation and partial autocorrelation methods and a suitable model to forecast TB infection was obtained.  The goodness of fit was measured using the Akaike Information Criteria (AIC) and Bayesian Information Criteria (BIC). Having satisfied all the model assumptions ARIMA (0,1,1) model with standard error, 6.37086 was found to be the best model for the forecast. It was observed that the forecasted series were close to the actual data series

scholarly journals Determine neighboring region spatial effect on dengue cases using ensemble ARIMA models

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Loshini Thiruchelvam ◽  
Sarat Chandra Dass ◽  
Vijanth Sagayan Asirvadam ◽  
Hanita Daud ◽  
Balvinder Singh Gill
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Epidemiological Data ◽  
The State ◽  
Spatial Effects ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Temperature Levels ◽  
Surrounding Areas

AbstractThe state of Selangor, in Malaysia consist of urban and peri-urban centres with good transportation system, and suitable temperature levels with high precipitations and humidity which make the state ideal for high number of dengue cases, annually. This study investigates if districts within the Selangor state do influence each other in determining pattern of dengue cases. Study compares two different models; the Autoregressive Integrated Moving Average (ARIMA) and Ensemble ARIMA models, using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) measurement to gauge their performance tools. ARIMA model is developed using the epidemiological data of dengue cases, whereas ensemble ARIMA incorporates the neighbouring regions’ dengue models as the exogenous variable (X), into traditional ARIMA model. Ensemble ARIMA models have better model fit compared to the basic ARIMA models by incorporating neighbuoring effects of seven districts which made of state of Selangor. The AIC and BIC values of ensemble ARIMA models to be smaller compared to traditional ARIMA counterpart models. Thus, study concludes that pattern of dengue cases for a district is subject to spatial effects of its neighbouring districts and number of dengue cases in the surrounding areas.

scholarly journals Implementation of Autoregressive Integrated Moving Average Model to Forecast Raw Material Stock in The Digital Printing Industry

2020 ◽  
Vol 5 (1) ◽  
pp. 13
Author(s):  
Dwi Asa Verano ◽  
Husnawati Husnawati ◽  
Ermatita Ermatita
Keyword(s):  
Raw Materials ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Raw Material ◽  
Digital Printing ◽  
Printing Industry ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model

The technology used in the printing industry is currently growing rapidly. Generally, the digital printing industry uses raw materials in the form of paper production. The use of paper material with large volumes is clear badly in need of purchasing large quantities of paper stock as well. The purchase of paper stocks with a constant amount at the beginning of each month for various types of paper causes a buildup or lack of material stock standard on certain types of paper. During this time the purchase and ordering of raw materials only based on the estimates or predictions of the owner. In this paper proposed forecasting will be carried out in the digital printing industry by applying the ARIMA model for each type of raw material paper with the Palembang F18 digital printing case study. The ARIMA modeling applied will produce different parameters for each materials paper type so as to produce forecasting with the Akaike Information Criterion (AIC) value averages 13.0294%.

COVID-19 ARIMA Model Forecast: Confirmed, Mortality & Recovered Cases in 13 Caribbean Countries (Preprint)

2020 ◽  
Author(s):  
Debjyoti Talukdar ◽  
Dr. Vrijesh Tripathi
Keyword(s):  
Time Series ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Local Health ◽  
Health Administration ◽  
Model Forecast ◽  
Time Period ◽  
The Impact

BACKGROUND Rapid spread of SARS nCoV-2 virus in Caribbean region has prompted heightened surveillance with more than 350,000 COVID-19 confirmed cases in 13 Caribbean countries namely Antigua and Barbados, Bahamas, Barbados, Cuba, Dominica, Dominican Republic, Grenada, Haiti, Jamaica, Saint Kitts and Nevis, Saint Lucia, Saint Vincent and the Grenadines, Trinidad and Tobago. OBJECTIVE The aim of our study is to analyze the impact of coronavirus (SARS nCoV-2) in 13 Caribbean countries in terms of confirmed cases, number of deaths and recovered cases. Current and projected forecasts using advanced autoregressive integrated moving average (ARIMA) models will enable local health organisations to plan future courses of action in terms of lockdown and managing essential public services. METHODS The study uses the auto regressive integrated moving average (ARIMA) model based upon time series pattern as per data retrieved from John Hopkins University, freely accessible on public domain and used for research and academic purposes. The data was analyzed using STATA 14 SE software between the time period - Jan 22, 2020 till May 27, 2020 using ARIMA time series analysis. It involves generalizing an autoregressive moving average model to better understand the data and predict future points in the time series until June 15, 2020. RESULTS The results show the predicted trend in terms of COVID-19 confirmed, mortality and recovered cases for 13 Caribbean countries. The projected ARIMA model forecast for the time period - May 25, 2020 to May 31, 2020 show 20278 (95% CI 19433.21 - 21123.08) confirmed cases, 631 (95% CI 615.90 - 646.51) deaths and 11501 (95% CI 10912.45 - 12089) recovered cases related to SARS nCoV-2 virus. The final ARIMA model chosen for confirmed COVID-19 cases, number of deaths and recovered cases are ARIMA (4,2,2), ARIMA (2,1,2) and ARIMA (4,1,2) respectively. All chosen models were compared with other models in terms of various factors like AIC/BIC (Akaike Information Criterion/Bayesian Information Criterion), log likelihood, p-value significance, coefficient < 1 and 5% significance. The autocorrelation function (ACF) and partial autocorrelation function (PACF) graphs were plotted to reduce bias and select the best fitting model. CONCLUSIONS As per the results of the forecasted COVID-19 models, there is a steady rise in terms of confirmed, recovered and mortality cases during the time period March 1, 2020 until May 27, 2020. It shows an increasing trend for confirmed and recovered COVID-19 cases and slowing of the number of mortality cases over a period of time. The predicted model will help the local health administration to devise public policies in terms of awareness measures, lockdown and essential health services accordingly.

Forecasting the spread and total size of confirmed and discharged cases of COVID-19 in Nigeria using an ARIMA model

2021 ◽  
pp. 1-6
Author(s):  
S. Agboola ◽  
P. Niyang ◽  
O. Olawepo ◽  
W. Ukponu ◽  
S. Niyang ◽  
...  
Keyword(s):  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Total Size ◽  
Autoregressive Integrated Moving Average ◽  
Daily Data ◽  
Partial Autocorrelation Function ◽  
The Government ◽  
Global Threat ◽  
Mandatory Use

Coronavirus disease 2019 (COVID-19) has been considered a global threat spreading to Nigeria and posing major public health threats and concerns. This led to the introduction of internationally acceptable non-pharmaceutical interventions (NPI) such as lockdowns, social distancing, and mandatory use of face masks by the Nigerian government to curtail the disease. This study aims to develop an Autoregressive Integrated Moving Average (ARIMA) model to predict COVID-19 cases vis Total Confirmed Cases (TCC) and Total Discharged Cases (TDC) in Nigeria based on the daily data obtained from the Nigeria Centre for Diseases Control (NCDC) from 27th February 2020 to 6th June 2020. The autocorrelation function (ACF), and partial autocorrelation function (PACF) were used to determine the constructed model. An ARIMA model was developed to predict the trend of TCC and TDC for the next 200 days. Forecasting was done using the constructed models. The finding shown that TCC increased to 50,225 with a CI between 29,425 to 100,450 and TDC to 20,186 with CI between 11,106 to 40,366 approximately. The result shows a significant increase in both TCC and TDC from COVID-19 which should guide the government roll out and management of the different NPI and policies to contain the virus.

scholarly journals Seasonal Variation and Time Trend Analysis of Dog Bite Cases Attending the Anti Rabies Clinic in Delhi using ARIMA Model Forecasting

2021 ◽  
Author(s):  
Neha Taneja ◽  
Vinoth Gnana Chellaiyan ◽  
Sujata Gupta ◽  
Rajesh Gupta ◽  
AY Nirupama
Keyword(s):  
Trend Analysis ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Viral Disease ◽  
Cross Sectional Study ◽  
Model Verification ◽  
Dog Bite ◽  
Cross Sectional

Introduction: Rabies is a fatal viral disease which is transmitted to humans through animal bites, most commonly via dogs. Fortunately, this disease is preventable through timely pre and postexposure vaccination. Aim: To study the seasonal predisposition and trend analysis of dog bite cases attending the anti rabies clinic. Materials and Methods: This retrospective cross-sectional study was conducted in the anti rabies clinic of a government hospital in Delhi. Enumeration and inclusion of all dog bite cases were made. An Autoregressive Integrated Moving Average (ARIMA) model was used to analyse the available data of dog bites, from 2011 to 2018. In this study, the least Bayesian Information Criterion (BIC) value was 12.2 and the corresponding model is ARIMA (1, 0, 0) with the goodness of fit 2 (R2=44%). The model verification was done by noise residual check. The model was applied for time series analysis and forecasting of rabies cases in subsequent years. Results: Total number of dog bite cases were 1,46,344 in last eight years (2011-2018). The maximum number of cases being 27961 in the year 2014 followed by 22385 in the year 2013. A seasonal predisposition of dog bite cases was seen for the month of February to April. The trend analysis forecasting for 2019, 2020, 2021, 2022 and 2023 predicted 11317, 11676, 10157, 8639 and 7120 cases, respectively. Conclusion: Although the dog bite cases will be on a decline in the future, adequate measures need to be strengthened further to sensitise the community about rabies prevention and timely reporting to anti rabies clinic for prophylaxis.

scholarly journals Optimization of ARIMA Forecasting Model using Firefly Algorithm

2019 ◽  
Vol 13 (2) ◽  
pp. 127 ◽  
Author(s):  
Ilham Unggara ◽  
Aina Musdholifah ◽  
Anny Kartika Sari
Keyword(s):  
Time Series ◽  
Firefly Algorithm ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Information Criterion ◽  
Forecasting Model ◽  
Optimal Model ◽  
Autoregressive Integrated Moving Average ◽  
Foreign Tourist

 Time series prediction aims to control or recognize the behavior of the system based on the data in a certain period of time. One of the most widely used method in time series prediction is ARIMA (Autoregressive Integrated Moving Average). However, ARIMA has a weakness in determining the optimal model. firefly algorithm is used to optimize ARIMA model (p, d, q). by finding the smallest AIC (Akaike Information Criterion) value in determining the best ARIMA model. The data used in the study are daily stock data JCI period January 2013 until August 2016 and data of foreign tourist visits to Indonesia period January 1988 to November 2017.Based on testing, for JCI data, obtained predicted results with Box-Jenkins ARIMA model produces RMSE 49.72, whereas the prediction with the ARIMA Optimization model yielded RMSE 49.48. For the data of Foreign Tourist Visits, the predicted results with the Box-Jenkins ARIMA model resulted in RMSE 46088.9, whereas the predicted results with ARIMA optimization resulted in RMSE 44678.4. From these results it can be concluded that the optimization of ARIMA model with Firefly Algorithm produces better forecasting model than ARIMA model without Optimization.

scholarly journals Stochastic ARIMA model for annual rainfall and maximum temperature forecasting over Tordzie watershed in Ghana

2018 ◽  
Vol 37 (1) ◽  
pp. 127-140 ◽  
Author(s):  
Mexoese Nyatuame ◽  
Sampson K. Agodzo
Keyword(s):  
Mean Square Error ◽  
Autocorrelation Function ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Annual Rainfall ◽  
Maximum Temperature ◽  
Complex Nature ◽  
Mean Square ◽  
Rainfall Prediction

AbstractThe forecast of rainfall and temperature is a difficult task due to their variability in time and space and also the inability to access all the parameters influencing rainfall of a region or locality. Their forecast is of relevance to agriculture and watershed management, which significantly contribute to the economy. Rainfall prediction requires mathematical modelling and simulation because of its extremely irregular and complex nature. Autoregressive integrated moving average (ARIMA) model was used to analyse annual rainfall and maximum temperature over Tordzie watershed and the forecast. Autocorrelation function (ACF) and partial autocorrelation function (PACF) were used to identify the models by aid of visual inspection. Stationarity tests were conducted using the augmented Dickey–Fuller (ADF), Mann–Kendall (MK) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) tests respectively. The chosen models were evaluated and validated using the Akaike information criterion corrected (AICC) and also Schwartz Bayesian criteria (SBC). The diagnostic analysis of the models comprised of the independence, normality, homoscedascity, P–P and Q–Q plots of the residuals respectively. The best ARIMA model for rainfall for Kpetoe and Tordzinu were (3, 0, 3) and (3, 1, 3) with AICC values of 190.07 and 178.23. That of maximum temperature for Kpetoe and Tordzinu were (3, 1, 3) and (3, 1, 3) and the corresponding AICC values of 23.81 and 36.10. The models efficiency was checked using sum of square error (SSE), mean square error (MSE), mean absolute percent error (MAPE) and root mean square error (RMSE) respectively. The results of the various analysis indicated that the models were adequate and can aid future water planning projections.

Sign in / Sign up

Export Citation Format

Share Document