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 OF UNMET NEEDS PERCENTAGE IN EAST JAVA PROVINCE USING AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) METHOD

2020 ◽  
Vol 9 (1) ◽  
pp. 53
Author(s):  
Feri Styaningsih
Keyword(s):  
Unmet Needs ◽  
Moving Average ◽  
Arima Model ◽  
Unmet Need ◽  
Small Error ◽  
Monthly Data ◽  
Specific Data ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method ◽  
Test Requirements

ARIMA uses present and past values as the dependent variable. The accuracy of the ARIMA forecasting method results is good to be used to obtain short-term forecasts. Compared to other time series methods, the advantage of ARIMA method is that it can be used in the percentage of unmet needs data in East Java Province since ARIMA method does not require any specific data motives. Unmet need is a group of women who do not want to have any more children or want to minimize their pregnancy but refuse to use contraception to prevent pregnancy. This study aims to determine the percentage of unmet needs in East Java Province in the future. This study will analyze the value of forecasting and determine the best model for ARIMA. The data used is the monthly data of unmet needs percentage of East Java Province starting from January 2014 to April 2019 (64 data plots). The results showed that the percentage of the number of unmet needs in East Java Province can be predicted using ARIMA model (12,1,0) without constant. The model is based on ARIMA (12,1,0) diagnostic test without constant meeting all the test requirements. The results of forecasting held a MAPE value of 2.369% and MAE of 0.26%. Based on MAPE and MAE, the model has a very good forecasting ability with a fairly small error value. Forecasting results indicated fluctuations in unmet needs data, where from December 2019 to February 2020 there was an increase in number of unmet needs in East Java Province. In the interim, starting in March 2020, the data needs in East Java Province tend to be constant at a higher position than the previous increase.

scholarly journals Time Series Analysis and Forecasting of Oilseeds Production in India: Using Autoregressive Integrated Moving Average and Group Method of Data Handling – Neural Network

2019 ◽  
pp. 1-14 ◽  
Author(s):  
Debasis Mithiya ◽  
Lakshmikanta Datta ◽  
Kumarjit Mandal
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Series Data ◽  
Percentage Error ◽  
Group Method ◽  
Data Handling ◽  
Agricultural Economy ◽  
Autoregressive Integrated Moving Average

Oilseeds have been the backbone of India’s agricultural economy since long. Oilseed crops play the second most important role in Indian agricultural economy, next to food grains, in terms of area and production. Oilseeds production in India has increased with time, however, the increasing demand for edible oils necessitated the imports in large quantities, leading to a substantial drain of foreign exchange. The need for addressing this deficit motivated a systematic study of the oilseeds economy to formulate appropriate strategies to bridge the demand-supply gap. In this study, an effort is made to forecast oilseeds production by using Autoregressive Integrated Moving Average (ARIMA) model, which is the most widely used model for forecasting time series. One of the main drawbacks of this model is the presumption of linearity. The Group Method of Data Handling (GMDH) model has also been applied for forecasting the oilseeds production because it contains nonlinear patterns. Both ARIMA and GMDH are mathematical models well-known for time series forecasting. The results obtained by the GMDH are compared with the results of ARIMA model. The comparison of modeling results shows that the GMDH model perform better than the ARIMA model in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The experimental results of both models indicate that the GMDH model is a powerful tool to handle the time series data and it provides a promising technique in time series forecasting methods.

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 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 Comparing Statistical Models to Predict Dengue Fever Notifications

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Arul Earnest ◽  
Say Beng Tan ◽  
Annelies Wilder-Smith ◽  
David Machin
Keyword(s):  
Dengue Fever ◽  
Statistical Models ◽  
Disease Surveillance ◽  
Moving Average ◽  
Arima Model ◽  
Public Health Problem ◽  
Percentage Error ◽  
Mosquito Vector ◽  
Autoregressive Integrated Moving Average ◽  
The Mean

Dengue fever (DF) is a serious public health problem in many parts of the world, and, in the absence of a vaccine, disease surveillance and mosquito vector eradication are important in controlling the spread of the disease. DF is primarily transmitted by the femaleAedes aegyptimosquito. We compared two statistical models that can be used in the surveillance and forecast of notifiable infectious diseases, namely, the Autoregressive Integrated Moving Average (ARIMA) model and the Knorr-Held two-component (K-H) model. The Mean Absolute Percentage Error (MAPE) was used to compare models. We developed the models using used data on DF notifications in Singapore from January 2001 till December 2006 and then validated the models with data from January 2007 till June 2008. The K-H model resulted in a slightly lower MAPE value of 17.21 as compared to the ARIMA model. We conclude that the models' performances are similar, but we found that the K-H model was relatively more difficult to fit in terms of the specification of the prior parameters and the relatively longer time taken to run the models.

scholarly journals A Forecasting Method Based on ARIMA Model for Best-Fitted Nutrition Water Supplement on Fruits

2019 ◽  
Vol 9 (2) ◽  
pp. 3167-3173
Keyword(s):  
Water Level ◽  
Moving Average ◽  
Arima Model ◽  
Environmental Parameters ◽  
Maximum Temperature ◽  
Sarima Model ◽  
Autoregressive Integrated Moving Average ◽  
Feature Vectors ◽  
Forecasting Method ◽  
Water Rate

The main focus of this research is to promote a forecasting method in the greenhouse of cultivation for the nutrition water level of strawberry fruits. In the greenhouse of cultivation, this study selects strawberry fruits as the focus on research. With adequate nutrition water supply conditions, the autoregressive integrated moving average and seasonal autoregressive integrated moving average (ARIMA-SARIMA) were utilized to create forecasting for the nutrition water level of strawberry leaves in the fruit greenhouse of cultivation, thus forecasting strawberry's nutrition water rate through greenhouse environmental parameters. Next, the multi-scale feature vectors of greenhouse temperature and nutrition water parameters in the greenhouse have been extracted by using the data pre-processing method to eliminate the testing and training value of variables, thus improving the forecasting and generalization ability of the model. The extracted feature vectors have been used to train and optimize the SARIMA model, finally obtaining the forecasting model of nutrition water rate of strawberry fruits leaves in the greenhouse of cultivation, which has been compared in experiments with the autoregressive integrated moving average and seasonal autoregressive integrated moving average (ARIMA - SARIMA) model. The results indicate that when training samples become a certain amount, the forecasting accuracy and regression fitting degree of ARIMA - SARIMA can be higher than that of the two traditional models. We forecasted that the strawberry greenhouse included 233 samples collected from a strawberry greenhouse in South Korea, and the 6 variables involved are greenhouse maximum temperature, greenhouse minimum temperature, greenhouse average temperature, quality of nutrient water, humanity, and CO2 , which would influence the strawberry growth in production concentration directly or indirectly with the variation of nutrition water every day.

scholarly journals Aplikasi Autoregressive Integrated Moving Average (ARIMA) untuk Meramalkan Jumlah Demam Berdarah Dengue (DBD) di Puskesmas Mulyorejo

2017 ◽  
Vol 5 (2) ◽  
pp. 177
Author(s):  
Luluk Nor Kasanah
Keyword(s):  
Public Health ◽  
Health Center ◽  
Dengue Hemorrhagic Fever ◽  
Moving Average ◽  
Arima Model ◽  
Hemorrhagic Fever ◽  
Small Error ◽  
Least Square ◽  
Public Health Center ◽  
Forecasting Method

ARIMA was one of a forecasting method of time series if independent variable be ignored, it would use the past and present value as a dependent variable. The accuracy of ARIMA forecasting method was good to produce short-term forecasting. The advantages of ARIMA method than other method was this method didn’t require the data pattern so it could be used for all kinds of data pattern, so it could be applied in cases of dengue hemorrhagic fever (DHF) in Mulyorejo Public Health Center. This study was to determine the best forecasting model as well as to predict and analyze the results of forecasting number of dengue hemorrhagic fever in Mulyorejo Public Health Center. The data was monthly number of dengue hemorrhagic fever patients in Mulyorejo Public Health Center from January 2010 to February 2016 (a total of 74 plots data). The results were the number of dengue hemorrhagic fever cases in Mulyorejo Public Health Center could be predicted with ARIMA model (1,0,0), thought based on diagnostics test the ARIMA model met all tests but the forecasting number of dengue hemorrhagic fever cases in years 2016–2017 showed a downward trend, and in 2017 was fl at, while MAPE and MAE amounted to 63.026% and 1.89%, the value of the error was large enough which indicated that less accurate forecasting. DHF data had a lot of missing data caused big value of MAPE and MAE so must be transformed by series mean method. DHF data was trend and seasonal so winters exponential smoothing with ordinary least square was better than ARIMA to get small error.

scholarly journals Comparison of algorithms for the prediction of glucose levels in patients with diabetes

Nova Scientia ◽  
2021 ◽  
Vol 13 (26) ◽  
Author(s):  
Daniel Arturo Olivares Vera ◽  
David Asael Gutiérrez Hernández ◽  
Marco Antonio Escobar Acevedo ◽  
Claudia Margarita Lara Rendón ◽  
Dulce Aurora Velázquez Velázquez
Keyword(s):  
Neural Networks ◽  
Moving Average ◽  
Arima Model ◽  
Percentage Error ◽  
Diabetic Patients ◽  
Autoregressive Integrated Moving Average ◽  
Glucose Levels ◽  
Univariate Time Series ◽  
Patients With Diabetes ◽  
History Of

This work presents a comparison between two algorithms for the prediction of glucose levels in diabetic patients by using a univariate time series. The algorithms are applied to the history of fasting glucose levels to predict the five following values. The comparison is performed between 1) The Autoregressive Neural Networks (ARNN) and 2) The autoregressive integrated moving average (ARIMA) models. A total of 70 series are analyzed, and we show that the results obtained for the ARIMA model have error percentages higher than 25% of the predicted value to the expected value. In contrast, in 73% of the cases, the percentage error was less than 25% for the Autoregressive Neural Networks.

scholarly journals Forecasting of Rabi Pulse Production in Odisha (India) by Using Autoregressive Integrated Moving Average (ARIMA) Technique

2020 ◽  
pp. 15-24
Author(s):  
Abhiram Dash ◽  
A. Mangaraju ◽  
Suman . ◽  
Pradeep Mishra
Keyword(s):  
Autocorrelation Function ◽  
Model Building ◽  
Moving Average ◽  
Arima Model ◽  
Original Data ◽  
Percentage Error ◽  
Arima Models ◽  
Autoregressive Integrated Moving Average ◽  
Fit Statistics ◽  
Testing Set

The present study was carried out to forecast the production of rabi pulse in Odisha by using the forecast values of area and yield of rabi pulses obtained from the selected best fit Autoregressive Integrated Moving Average (ARIMA) model. The data from 1971-72 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 and/or the differenced data. The different ARIMA models are judged 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. The best fitted models are selected on the basis of residual diagnostics test and model fit statistics. The ARIMA model found to be best fitted for area under rabi pulse and yield of rabi pulse are ARIMA (2,0,0) with constant and ARIMA (0,1,1) without constant respectively which are successfully cross-validated with the testing set data. The excellent fit ARIMA model has been used to forecast the area and yield of rabi pulse for the years 2016-17, 2017-18 and 2018-19. The forecast value of area shows an increase, where as, the forecast values of yield shows a decrease. The forecast values of production of rabi pulse obtained from the forecast values of area and yield of rabi pulse shows an increase which is due to the increase in forecast value of area. Thus emphasis must be laid on increasing the future yield of rabi pulse so as to achieve sufficient increase in production of rabi pulses which could ensure nutritional security to more extent.

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