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

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 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.

scholarly journals Predicting Seasonal Influenza Based on SARIMA Model, in Mainland China from 2005 to 2018

2019 ◽  
Vol 16 (23) ◽  
pp. 4760 ◽  
Author(s):  
Cong ◽  
Ren ◽  
Xie ◽  
Wang
Keyword(s):  
Seasonal Influenza ◽  
Moving Average ◽  
Mainland China ◽  
Seasonal Fluctuation ◽  
Influenza Surveillance ◽  
Sarima Model ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Predicted Values ◽  
Relative Errors

Seasonal influenza is one of the mandatorily monitored infectious diseases, in China. Making full use of the influenza surveillance data helps to predict seasonal influenza. In this study, a seasonal autoregressive integrated moving average (SARIMA) model was used to predict the influenza changes by analyzing monthly data of influenza incidence from January 2005 to December 2018, in China. The inter-annual incidence rate fluctuated from 2.76 to 55.07 per 100,000 individuals. The SARIMA (1, 0, 0) × (0, 1, 1) 12 model predicted that the influenza incidence in 2018 was similar to that of previous years, and it fitted the seasonal fluctuation. The relative errors between actual values and predicted values fluctuated from 0.0010 to 0.0137, which indicated that the predicted values matched the actual values well. This study demonstrated that the SARIMA model could effectively make short-term predictions of seasonal influenza.

scholarly journals Indian COVID-19 dynamics: Prediction using autoregressive integrated moving average modelling

2021 ◽  
Vol 52 (1) ◽  
pp. 6-14
Author(s):  
Amit Tak ◽  
Sunita Dia ◽  
Mahendra Dia ◽  
Todd Wehner
Keyword(s):  
Root Mean Square Error ◽  
Real Time ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Moving Average ◽  
Information Criteria ◽  
Evidence Based ◽  
Mean Square ◽  
Autoregressive Integrated Moving Average ◽  
Akaike Information Criteria

Background: The forecasting of Coronavirus Disease-19 (COVID-19) dynamics is a centrepiece in evidence-based disease management. Numerous approaches that use mathematical modelling have been used to predict the outcome of the pandemic, including data-driven models, empirical and hybrid models. This study was aimed at prediction of COVID-19 evolution in India using a model based on autoregressive integrated moving average (ARIMA). Material and Methods: Real-time Indian data of cumulative cases and deaths of COVID-19 was retrieved from the Johns Hopkins dashboard. The dataset from 11 March 2020 to 25 June 2020 (n = 107 time points) was used to fit the autoregressive integrated moving average model. The model with minimum Akaike Information Criteria was used for forecasting. The predicted root mean square error (PredRMSE) and base root mean square error (BaseRMSE) were used to validate the model. Results: The ARIMA (1,3,2) and ARIMA (3,3,1) model fit best for cumulative cases and deaths, respectively, with minimum Akaike Information Criteria. The prediction of cumulative cases and deaths for next 10 days from 26 June 2020 to 5 July 2020 showed a trend toward continuous increment. The PredRMSE and BaseRMSE of ARIMA (1,3,2) model were 21,137 and 166,330, respectively. Similarly, PredRMSE and BaseRMSE of ARIMA (3,3,1) model were 668.7 and 5,431, respectively. Conclusion: It is proposed that data on COVID-19 be collected continuously, and that forecasting continue in real time. The COVID-19 forecast assist government in resource optimisation and evidence-based decision making for a subsequent state of affairs.

scholarly journals Autoregressive Integrated Moving Average (ARIMA) Sebagai Model Peramalan Kasus Demam Berdarah Dengue

2020 ◽  
Vol 10 (2) ◽  
pp. 76-80
Author(s):  
Roro Kushartanti ◽  
Maulina Latifah
Keyword(s):  
Community Health ◽  
Health Center ◽  
Moving Average ◽  
Arima Model ◽  
Community Health Center ◽  
Percentage Error ◽  
Dengue Case ◽  
Monthly Data ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method

ARIMA is a forecasting method time series that does not require a specific data pattern. This study aims to analyze the forecasting of Semarang City DHF cases specifically in the Rowosari Community Health Center. The study used monthly data on DHF cases in the Rowosari Community Health Center in 2016, 2017, and 2019 as many as 36 dengue case data. The best ARIMA model for forecasting is a model that meets the requirements for parameter significance, white noise and has the MAPE (Mean Absolute Percentage Error Smallest) value. The results of the analysis show that the best model for predicting the number of dengue cases in the Rowosari Public Health Center Semarang is the ARIMA model (1,0,0) with a MAPE value of 43.98% and a significance coefficient of 0.353, meaning that this model is suitable and feasible to be used as a forecasting model. DHF cases in the Rowosari Community Health Center in Semarang City.

scholarly journals Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Rahul Tripathi ◽  
A. K. Nayak ◽  
R. Raja ◽  
Mohammad Shahid ◽  
Anjani Kumar ◽  
...  
Keyword(s):  
Autocorrelation Function ◽  
Moving Average ◽  
Selection Criterion ◽  
Information Criteria ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Rice Productivity ◽  
Rice Area ◽  
Moving Average Models ◽  
Partial Autocorrelation Function

Forecasting of rice area, production, and productivity of Odisha was made from the historical data of 1950-51 to 2008-09 by using univariate autoregressive integrated moving average (ARIMA) models and was compared with the forecasted all Indian data. The autoregressive (p) and moving average (q) parameters were identified based on the significant spikes in the plots of partial autocorrelation function (PACF) and autocorrelation function (ACF) of the different time series. ARIMA (2, 1, 0) model was found suitable for all Indian rice productivity and production, whereas ARIMA (1, 1, 1) was best fitted for forecasting of rice productivity and production in Odisha. Prediction was made for the immediate next three years, that is, 2007-08, 2008-09, and 2009-10, using the best fitted ARIMA models based on minimum value of the selection criterion, that is, Akaike information criteria (AIC) and Schwarz-Bayesian information criteria (SBC). The performances of models were validated by comparing with percentage deviation from the actual values and mean absolute percent error (MAPE), which was found to be 0.61 and 2.99% for the area under rice in Odisha and India, respectively. Similarly for prediction of rice production and productivity in Odisha and India, the MAPE was found to be less than 6%.

scholarly journals A SARIMA forecasting model to predict the number of cases of dengue in Campinas, State of São Paulo, Brazil

2011 ◽  
Vol 44 (4) ◽  
pp. 436-440 ◽  
Author(s):  
Edson Zangiacomi Martinez ◽  
Elisângela Aparecida Soares da Silva ◽  
Amaury Lelis Dal Fabbro
Keyword(s):  
Moving Average ◽  
Forecasting Model ◽  
Time Series Models ◽  
R Software ◽  
Relative Precision ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Predicted Values ◽  
Best Fit ◽  
Monthly Incidence

INTRODUCTION: Forecasting dengue cases in a population by using time-series models can provide useful information that can be used to facilitate the planning of public health interventions. The objective of this article was to develop a forecasting model for dengue incidence in Campinas, southeast Brazil, considering the Box-Jenkins modeling approach. METHODS: The forecasting model for dengue incidence was performed with R software using the seasonal autoregressive integrated moving average (SARIMA) model. We fitted a model based on the reported monthly incidence of dengue from 1998 to 2008, and we validated the model using the data collected between January and December of 2009. RESULTS: SARIMA (2,1,2) (1,1,1)12 was the model with the best fit for data. This model indicated that the number of dengue cases in a given month can be estimated by the number of dengue cases occurring one, two and twelve months prior. The predicted values for 2009 are relatively close to the observed values. CONCLUSIONS: The results of this article indicate that SARIMA models are useful tools for monitoring dengue incidence. We also observe that the SARIMA model is capable of representing with relative precision the number of cases in a next year.

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 Analysis of the demand for gastronomic tourism in Andalusia (Spain)

PLoS ONE ◽  
2021 ◽  
Vol 16 (2) ◽  
pp. e0246377
Author(s):  
Mª Genoveva Dancausa Millán ◽  
Mª Genoveva Millán Vázquez de la Torre ◽  
Ricardo Hernández Rojas
Keyword(s):  
Olive Oil ◽  
Raw Materials ◽  
Moving Average ◽  
Food Products ◽  
Southern Spain ◽  
Autoregressive Integrated Moving Average ◽  
Wine Tourism ◽  
Protected Designations Of Origin ◽  
Sarima Models

In recent years, gastronomy has become a fundamental motivation to travel. Learning how to prepare gastronomic dishes and about the raw materials that compose them has attracted increasing numbers of tourists. In Andalusia (region of southern Spain), there are many quality products endorsed by Protected Designations of Origin, around which gastronomic routes have been created, some visited often (e.g., wine) and others remaining unknown (e.g., ham and oil). This study analyses the profile of gastronomic tourists in Andalusia to understand their motivations and estimates the demand for gastronomic tourism using seasonal autoregressive integrated moving average (SARIMA) models. The results obtained indicate that the gastronomic tourist in Andalusia is very satisfied with the places he/she visits and the gastronomy he/she savours. However, the demand for this tourist sector is very low and heterogeneous; while wine tourism is well established, tourism focusing on certain products, such as olive oil or ham, is practically non-existent. To obtain a homogeneous demand, synergies or pairings should be created between food products, e.g., wine-ham, oil-ham, etc., to attract a greater number of tourists and distinguish Andalusia as a gastronomic holiday destination.

scholarly journals Forecasting of salmonellosis epidemic proces in Ukraine using autoregressive integrated moving average model

2020 ◽  
pp. 346-354
Author(s):  
Serhii Bohdanov ◽  
Yulia Polyvianna ◽  
Tetyana Chumachenko ◽  
Dmytro Chumachenko
Keyword(s):  
Moving Average ◽  
Incidence Rates ◽  
Autoregressive Moving Average ◽  
Average Model ◽  
Autoregressive Integrated Moving Average ◽  
Autoregressive Moving Average Model ◽  
Moving Average Model ◽  
Complex Models ◽  
Optimal Values ◽  
Predicted Values

The article highlights the problem of salmonellosis among the population of the Kharkov region, Ukraine. Three time series were used for calculations: a series of incidence rates for men, a series of incidence rates for women and a series of incidence rates for the general population, each of the series was an ordered set of monthly values from December 2015 to December 2018. It was revealed that the most effective tool for analyzing these statistical data is the use of the autoregressive moving average model (ARIMA). The following steps were used: identification and replacement of outliers, the use of smoothing and decomposition of the series. The developed model allows you to explicitly indicate the order of the model using the arima () function or automatically generate a set of optimal values (p, d, q) using the auto.arima () function. The validated model allows to calculate the predicted values of the incidence of salmonellosis for 50 days. In certain cases, models of exponential smoothing are able to give forecasts that are not inferior in accuracy to forecasts obtained using more complex models.

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