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 Forecasting wholesale price of coarse rice in Bangladesh: A seasonal autoregressive integrated moving average approach

2014 ◽  
Vol 11 (2) ◽  
pp. 271-276
Author(s):  
MF Hassan ◽  
MA Islam ◽  
MF Imam ◽  
SM Sayem
Keyword(s):  
Moving Average ◽  
Wholesale Price ◽  
Information Criteria ◽  
Suitable Model ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Lag Times ◽  
Predicted Values ◽  
Absolute Percent Error ◽  
Sarima Models

This article attempts to develop the model and to forecast the wholesale price of coarse rice in Bangladesh. Seasonal Autoregressive Integrated Moving Average (SARIMA) models have been developed on the monthly data collected from July 1975 to December 2011and validated using the data from December 2010 to December 2011. The results showed that the predicted values were consistent with the upturns and downturns of the observed series. The model with non seasonal autoregressive 1, difference 1 and moving average 1 and seasonal difference 1 and moving average 1 that is SARIMA (1,1,1)(0,1,1)12 model has been found as the most suitable model with least Root Mean Square Error (RMSE) of 61.657, Normalised Bayesian Information Criteria (BIC) of 8.300 and Mean Absolute Percent Error (MAPE) of 3.906. The model was further validated by Ljung-Box test (Q18=17.394 and p>.20) with no significant autocorrelation between residuals at different lag times. Finally, a forecast for the period January 2012 to December 2013 was made. DOI: http://dx.doi.org/10.3329/jbau.v11i2.19925 J. Bangladesh Agril. Univ. 11(2): 271-276, 2013

scholarly journals Detection of Tea Export Potentiality from Bangladesh in the World Market: An Autoregressive Integrated Moving Average (ARIMA) Approach

2020 ◽  
pp. 21-29
Author(s):  
A. U. Noman ◽  
S. Majumder ◽  
M. F. Imam ◽  
M. J. Hossain ◽  
F. Elahi ◽  
...  
Keyword(s):  
Mean Absolute Error ◽  
Moving Average ◽  
Arima Model ◽  
Absolute Error ◽  
World Market ◽  
Information Criteria ◽  
Coefficient Of Determination ◽  
Percentage Error ◽  
Autoregressive Integrated Moving Average ◽  
The World

Export plays an important role in promoting economic growth and development. The study is conducted to make an efficient forecasting of tea export from Bangladesh for mitigating the risk of export in the world market. Forecasting has been done by fitting Box-Jenkins type autoregressive integrated moving average (ARIMA) model. The best ARIMA model is selected by comparing the criteria- coefficient of determination (R2), root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute error (MAE) and Bayesian information criteria (BIC). Among the Box-Jenkins ARIMA type models for tea export the ARIMA (1,1,3) model is the most appropriate one for forecasting and the forecast values in thousand kilogram for the year 2017-18, 2018-19, 2019-20, 2020-21 and 2021-22, are 1096.48, 812.83, 1122.02, 776.25 and 794.33 with upper limit 1819.70, 1348.96, 1862.09, 1288.25, 1318.26 and lower limit 660.69, 489.78, 676.08, 467.74, 478.63, respectively. So, the result of this model may be helpful for the policymaker to make an export development plan for the country.

scholarly journals Forecasting Monthly Gold Prices using ARIMA Model: Evidence from Indian Gold Market

2019 ◽  
Vol 8 (9S3) ◽  
pp. 1373-1376
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Gold Price ◽  
Data Series ◽  
Suitable Model ◽  
Natural Logarithm ◽  
Price Series ◽  
Gold Market ◽  
Sample Data ◽  
Augmented Dickey Fuller

In this paper an attempt has been made to give an overview of the Indian gold market so as to develop a model enabling the forecast of gold prices in India. One troy ounce is equal to 31.103 grams. The monthly sample data of gold price (in INR per troy ounce) is taken from December 1997 to December 2017.The entire data has been divided into two segments for estimation and validation sample and to find out the efficiency and accuracy of forecasting models. Since the gold price data series have shown much deviation after March 2006 the first segment of the data is taken from the time period of December 1997 to March 2006 and second segment from April 2006 to December 2017.Due to a larger value and a huge time span of the sample data, the natural logarithm of gold price has been taken to conduct the study and build an effective model to forecast future gold prices. The unit root tests of Augmented Dickey Fuller‖ and Philips Perron have been used to test the gold price series as stationary or non-stationary. It is observed that series are stationary at first difference in both the methods. At first difference the ACFs and PACFs were pattern less and statistically not significant. Box-Jenkins’s Autoregressive Integrated Moving Average of Box-Jenkins methodology has been used for developing a forecasting model of gold price in India. Different models of ARIMA have been used to obtain best suitable model for forecasting using Eviews software 10 for both time periods i.e., December 1997 to March 2006 & April 2006 to December 2017

scholarly journals An Assessment of Renewable Energy in Bangladesh through ARIMA, Holt’s, ARCH-GARCH Models

2012 ◽  
Vol 60 (2) ◽  
pp. 159-162
Author(s):  
Fatema Tuz Jhohura ◽  
Md. Israt Rayhan
Keyword(s):  
Time Series ◽  
Renewable Energy ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Industrial Sector ◽  
Exponential Smoothing ◽  
Information Criteria ◽  
Optimal Decision ◽  
Autoregressive Integrated Moving Average ◽  
Moving Average Model

Forecasting of the Renewable Energy plays a major role in optimal decision formula for government and industrial sector in Bangladesh. This research is based on time series modeling with special application to solar energy data for Dhaka city. Three families of time series models namely, the autoregressive integrated moving average models, Holt’s linear exponential smoothing, and the autoregressive conditional heteroscedastic (with their extensions to generalized autoregressive conditional heteroscedastic) models were fitted to the data. The goodness of fit is performed via the Akaike information criteria, Schwartz Bayesian criteria. It was established that the generalized autoregressive conditional heteroscedastic model was superior to the autoregressive integrated moving average model and Holt’s linear exponential smoothing because the data was characterized by changing mean and variance.DOI: http://dx.doi.org/10.3329/dujs.v60i2.11486 Dhaka Univ. J. Sci. 60(2): 159-162, 2012 (July)

scholarly journals Forecasting of Potato Prices in India: An Application of Arima Model

2020 ◽  
Vol 65 (4) ◽  
Author(s):  
Ravi Ranjan Kumar
Keyword(s):  
West Bengal ◽  
Time Series Data ◽  
Moving Average ◽  
Arima Model ◽  
Information Criteria ◽  
Madhya Pradesh ◽  
Series Data ◽  
Market Prices ◽  
Autoregressive Integrated Moving Average ◽  
Absolute Percent Error

In the present paper, Autoregressive Integrated Moving Average (ARIMA) models developed to forecast the prices of potato using time series data of eighteen years from 2002-2019. The best models selected by comparing Akaike Information Criteria (AIC), Bayesian Information Criteria (BIC), Mean Absolute Percent Error (MAPE), and Root Mean Square Error (RMSE). The study revealed that ARIMA (1,1,2), ARIMA (2,1,1)(0,0,2)[12], ARIMA (2,1,2), ARIMA (1,1,4)(0,0,1)[12], ARIMA (1,1,1)(0,1,2)[12], ARIMA (0,1,0)(0,1,1)[12], and ARIMA (3,1,3) were the best fitted models for forecasting of price of potato for the states of Utter Pradesh, West Bengal, Madhya Pradesh, Gujarat, Punjab, Tripura and India respectively. The prices of potato in Utter Pradesh, West Bengal and India will be increasing with the first-quarter providing the highest price. The prices of potato in Madhya Pradesh and Tripura will be highest in the fourth quarter. In Punjab, the prices of potato will be increasing with the third-quarter. The forecast shows that market prices of potato in Utter Pradesh, West Bengal, Madhya Pradesh, Gujarat, Punjab, Tripura, and overall India would be ruling in the highest value of .1208 `/qt, 1812 `/qt, 1345 `/qt, 1712 `/qt, 1354 `/qt, 2636 `/qt, and 1715 `/qt respectively for the year 2020.

Periodic occurrences of annual rainfalls in Eastern India [UPH No. 9 (theme: Variability of extremes) and UPH No.19 (theme: Modelling methods)]

2020 ◽  
Author(s):  
Dr Subhabrata Panda
Keyword(s):  
Polynomial Regression ◽  
Moving Average ◽  
Arima Model ◽  
Rainfall Series ◽  
Rainfall Data ◽  
Annual Rainfall ◽  
Data Series ◽  
Eastern India ◽  
Autoregressive Integrated Moving Average ◽  
The Mean

<p>Long period annual rainfall data series from nine raingauge stations throughout eastern India were analysed. Those data series were for the years 1901 to 1965 for Aijal (Mizoram); 1901 to 1984 for Imphal (Manipur); 1901 to 1986 for Guwahati (Assam), Shillong, Cherrapunji (Meghalaya); 1901 to 1987 for Cuttack (Odisha), Patna (Bihar), Agartala (Tripura), Krishnanagar (West Bengal). Incomplete annual rainfall data were found out by taking average of data of preceding and following years. Each annual rainfall series was divided into modelled period (1901 to 1980 for eight stations except Aijal with 1901 to 1960) and predicted period (data for years left in the series after modelled period for evaluation of the model for prediction of future rainfalls). Each annual rainfall series in the modelled period was first converted into percentage values of the mean annual rainfall and then plotted against year, which showed the oscillations of the historigram about the mean line (Tomlinson, 1987 for New Zealand rainfalls). Such type of characteristic historigrams for all stations showed periodic nature of annual rainfalls throughout eastern India. So, autoregressive integrated moving average (ARIMA) model (Clarke, 1973) was used to evolve a useful model for prediction of future rainfalls. As the ARIMA model was biased for periodicity due to inclusion of both the ‘sin’ and ‘cos’ functions and period length as 12, modelled data series were analysed for polynomial regression. The accepted degrees of polynomials were decided on the basis of analysis of variance (ANOVA). Acceptance of either ARIMA model or polynomial regression was done on the basis of -test. In most of the cases in the observed historigrams the lengths of periods were less than eight years and in some cases those were eight to 12 years and from polynomial regressions in most cases the period lengths varied in between 8 to 12 years, 13 to 22 years and 23 to 37 years; and in rare cases those lengths were 38 years and more. Considering all the limitations in the observed data and 95% confidence interval for ARIMA model, a particular amount of annual rainfall occurred at about 12 years (i.e. almost resembling a Solar Cycle) and that might be concluded after minute analysis of more observed data. Recurrence of flood and drought years can be predicted from such analysis and also by following probability analysis of excess and deficit runs of annual rainfalls (Panda <em>et al</em>., 1996).</p><p>References:</p><p>Clarke, R.T.1973. Mathematical models in hydrology. FAO Irrigation and Drainage Paper No. 19. FAO of the United Nations, Rome. pp.101-108.</p><p>Panda, S.; Datta, D.K. and Das, M.N. (1996). Prediction of drought and flood years in Eastern India using length of runs of annual rainfall. J. Soil Wat. Conserv. India. 40(3&4):184-191.</p><p>          https://www.academia.edu/15034719/Prediction_of_drought_and_flood_years_in_eastern_%20%09India%20using_length_of_runs_of_annual_rainfall</p><p>Tomlinson, A.I. (1987). Wet and dry years – seven years on. Soil & Water. Winter 1987: 8-9. ISSN 0038-0695    </p>

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.

scholarly journals Forecasting Zambia’s Gross Domestic Product Using Time Series Autoregressive Integrated Moving Average (ARIMA) Model

2020 ◽  
Vol 5 (9) ◽  
pp. 440-447
Author(s):  
Chikumbe Evans Sankwa ◽  
Sikota Sharper
Keyword(s):  
Time Series ◽  
Gross Domestic Product ◽  
Moving Average ◽  
Social Indicators ◽  
Arima Model ◽  
Information Criteria ◽  
Domestic Product ◽  
Data Set ◽  
Autoregressive Integrated Moving Average ◽  
Study Results

Gross Domestic Product is one of the social indicators of development. This study attempts to model Zambia’s Gross domestic product using the Autoregressive Integrated Moving Average (ARIMA) model. This model has proved to help many countries during economic recession or when there is any disruption in the economic system due to pandemics or natural disasters. The study utilized a time series dataset from 1960 to 2018. The best model that fit the data set, following the selection model criteria, was ARIMA (5,2,0) model with the lowest Akaike’s Information Criteria(AIC) and Bayesian Information Criteria (BIC) and smallest volatility. The study results showed that, on average, Zambia’s gross domestic product will continue to rise over the next eight years. However, few recession (decline) points are expected in the period 2020 to 2022. It is hoped that the forecasts would be useful for researchers in Zambia, including the fiscal and monetary policy makers.

scholarly journals Container Throughput Forecasting Using Dynamic Factor Analysis and ARIMAX Model

2017 ◽  
Vol 29 (5) ◽  
pp. 529-542 ◽  
Author(s):  
Marko Intihar ◽  
Tomaž Kramberger ◽  
Dejan Dragan
Keyword(s):  
Factor Analysis ◽  
Goodness Of Fit ◽  
Moving Average ◽  
Real Data ◽  
Information Criteria ◽  
Dynamic Factor Analysis ◽  
Forecasting Model ◽  
Dynamic Factor ◽  
Macroeconomic Indicators ◽  
The Impact

The paper examines the impact of integration of macroeconomic indicators on the accuracy of container throughput time series forecasting model. For this purpose, a Dynamic factor analysis and AutoRegressive Integrated Moving-Average model with eXogenous inputs (ARIMAX) are used. Both methodologies are integrated into a novel four-stage heuristic procedure. Firstly, dynamic factors are extracted from external macroeconomic indicators influencing the observed throughput. Secondly, the family of ARIMAX models of different orders is generated based on the derived factors. In the third stage, the diagnostic and goodness-of-fit testing is applied, which includes statistical criteria such as fit performance, information criteria, and parsimony. Finally, the best model is heuristically selected and tested on the real data of the Port of Koper. The results show that by applying macroeconomic indicators into the forecasting model, more accurate future throughput forecasts can be achieved. The model is also used to produce future forecasts for the next four years indicating a more oscillatory behaviour in (2018-2020). Hence, care must be taken concerning any bigger investment decisions initiated from the management side. It is believed that the proposed model might be a useful reinforcement of the existing forecasting module in the observed port.

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