scholarly journals Perbandingan Metode Arima Box-Jenkins dan Holt-Winters No Seasonal pada Peramalan Jumlah Penderita ISPA di Kota Malang

2021 ◽  
Vol 11 (2) ◽  
pp. 72-77
Author(s):  
Nanta Sigit ◽  
Arief Setiyoargo
Keyword(s):  
Moving Average ◽  
Health Department ◽  
Forecasting Model ◽  
Acute Respiratory Disease ◽  
Estimation Model ◽  
Autoregressive Integrated Moving Average ◽  
Analytical Technique ◽  
Additional Information ◽  
Forecasting Method ◽  
Using Data

ISPA is an acute respiratory disease with special attention to pneumonia (ISPA), and not an ear and throat disease. In order to make plans to reduce the number of ISPA sufferers in the regions with effective and responsible principles, valid forecasts are needed. There is a relatively large difference between targets and achievements in tackling ISPA sufferers in Malang City during 2017 - 2020, and given the importance of forecasting as an indicator of reducing ISPA sufferers, it is deemed necessary to conduct research on the application of the Box-Jenkins model in forecasting ISPA patients. This study aims to create an estimation model for patients with ISPA in Malang Regency using data from the Health Office from 2017 to 2020. The analytical technique applied is the Box-Jenkins model or the Autoregressive Integrated Moving Average (ARIMA). The results showed that by using data from the Malang City Health Office from 2014 to 2019, it was concluded that the best forecasting model was ARIMA(1,1,0). Researchers hope that the forecasting method and forecasting results can be used as additional information for the health department in Malang City in determining policies that must be taken in the prevention of ISPA sufferers according to the needs of patients in Malang City.

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.

A comparative study of univariate models for container throughput forecasting of major ports in Asia

2021 ◽  
pp. 147509022110236
Author(s):  
Juan Huang ◽  
Ching-Wu Chu ◽  
Hsiu-Li Hsu
Keyword(s):  
Regression Model ◽  
Moving Average ◽  
Forecasting Model ◽  
Short Term ◽  
Autoregressive Integrated Moving Average ◽  
Port Authorities ◽  
Grey Forecasting ◽  
Grey Forecasting Model ◽  
Short Term Forecasting ◽  
Trigonometric Regression

This study aims to make comparisons on different univariate forecasting methods and provides a more accurate short-term forecasting model on the container throughput for rendering a reference to relevant authorities. We collected monthly data regarding container throughput volumes for three major ports in Asia, Shanghai, Singapore, and Busan Ports. Six different univariate methods, including the grey forecasting model, the hybrid grey forecasting model, the multiplicative decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, and the seasonal autoregressive integrated moving average (SARIMA) model, were used. We found that the hybrid grey forecasting model outperforms the other univariate models. This study’s findings can provide a more accurate short-term forecasting model for container throughput to create a reference for port authorities.

A Forecasting Method for Fertilizers Consumption in Brazil

2013 ◽  
Vol 4 (2) ◽  
pp. 23-36 ◽  
Author(s):  
Eduardo Ogasawara ◽  
Daniel de Oliveira ◽  
Fabio Paschoal Junior ◽  
Rafael Castaneda ◽  
Myrna Amorim ◽  
...  
Keyword(s):  
Large Scale ◽  
Moving Average ◽  
Logistic Function ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method ◽  
The World ◽  
Entire Country ◽  
Function Models ◽  
Nitrogen Phosphorus

Tracking information about fertilizers consumption in the world is very important since they are used to produce agriculture commodities. Brazil consumes a large amount of fertilizers due to its large-scale agriculture fields. Most of these fertilizers are currently imported. The analysis of consumption of major fertilizers, such as Nitrogen-Phosphorus-Potassium (NPK), Sulfur, Phosphate Rock, Potash, and Nitrogen become critical for long-term government decisions. In this paper we present a method for fertilizers consumption forecasting based on both Autoregressive Integrated Moving Average (ARIMA) and logistic function models. Our method was used to forecast fertilizers consumption in Brazil for the next 20 years considering different economic growth for the entire country.

The Forecasting of the Elevator Traffic Flow Time Series Based on ARIMA and GP

2012 ◽  
Vol 588-589 ◽  
pp. 1466-1471 ◽  
Author(s):  
Jun Fang Li ◽  
Qun Zong
Keyword(s):  
Time Series ◽  
Traffic Flow ◽  
Linear Models ◽  
Moving Average ◽  
Arima Model ◽  
Flow Time ◽  
Forecasting Model ◽  
Autoregressive Integrated Moving Average ◽  
Hybrid Forecasting ◽  
Artificial Neural Network Ann

As one of the conventional statistical methods, the autoregressive integrated moving average (ARIMA) model has been one of the most widely used linear models in time series forecasting. However, the ARIMA model cannot easily capture the nonlinear patterns. Artificial neural network (ANN) can be utilized to construct more accurate forecasting model than ARIMA for nonlinear time series, but it is difficult to explain the meaning of the hidden layers of ANN and it does not produce a mathematical equation. In this study, by combining ARIMA with genetic programming (GP), a hybrid forecasting model will be used for elevator traffic flow time series which can improve the accuracy both the GP and the ARIMA forecasting models separately. At last, simulations are adopted to demonstrate the advantages of the proposed ARIMA-GP forecasting model.

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 Forecasting Import Demand of Table Grapes: Empirical Evidence from Thailand

2020 ◽  
Vol 10 (2) ◽  
pp. 578-586
Author(s):  
Chalermpon Jatuporn ◽  
Patana Sukprasert ◽  
Siros Tongchure ◽  
Vasu Suvanvihok ◽  
Supat Thongkaew
Keyword(s):  
Time Series ◽  
Empirical Evidence ◽  
Unit Root ◽  
Moving Average ◽  
Unit Root Test ◽  
Import Demand ◽  
Forecasting Model ◽  
Autoregressive Integrated Moving Average ◽  
Table Grapes ◽  
Monthly Time Series

The purpose of this study is to forecast the import demand of table grapes of Thailand using monthly time series from January 2007 to April 2020. The ADF unit root test is used for stationarity checking, and seasonal autoregressive integrated moving average (SARIMA) is applied to forecast the import demand of table grapes. The results revealed that the integration of time series was in the first difference for non-seasonal and seasonal order. The best-fitted forecasting model was SARIMA(1,1,3)(2,1,0)12. The forecasted period for the next eight months showed the import demand of table grapes of Thailand that is slightly decreased by an average of 11.398 percent, with overall expected to decrease by an average of 15.218 percent in 2020.

Pemodelan Prakiraan Kas Pemerintah

2017 ◽  
Vol 2 (3) ◽  
pp. 1-11
Author(s):  
NFN Iskandar
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Government Policies ◽  
Forecasting Model ◽  
Cash Management ◽  
Autoregressive Integrated Moving Average ◽  
The Public ◽  
Greek Crisis ◽  
Artificial Neural Network Ann

ABSTRACT Government cash management refers to the strategies for managing government money to fulfil governments’ obligations effectively. Failure to manage cash effectively risks undermining the implementation of government policies. The Greek crisis in 2010 is an example of a government failing to manage resources effectively. Despite the importance of effective government cash management, the literature on effective cash forecasting, as one of effective government cash management’s pillars, in the public sector is scarce. This paper addresses this shortcoming by developing a government cash forecasting model with an accuracy that meets acceptable levels of materiality for the cash manager. Using Indonesian government expenditures data in a case study, we utilise Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) to build cash forecasting models. The results provide evidence that the ANN method is superior then the ARIMA model to build a government cash forecasting model. ABSTRAK Pengelolaan Kas Pemerintah mengacu pada serangkaian strategi yang dilakukan oleh pemerintah dalam mengelola uang pemerintah secara efektif dalam rangka memenuhi kewajiban pemerintah. Kegagalan dalam mengelola uang pemerintah secara efektif beresiko mengganggu pelaksanaan kebijakan pemerintah. Krisis yang dialami Yunani di tahun 2010 merupakan salah satu contoh dampak yang dapat ditimbulkan dari tidak berhasilnya suatu pemerintahan mengelola sumber daya keuangan yang mereka milik secara efektif. Terlepas dari pentingnya mengelola kas pemerintah secara efektif, literatur tentang bagaimana menyusun prakiraan kas yang efektif – sebagai salah satu pilar Pengelolaan Kas Pemerintah – bagi sektor publik masih langka. Penelitian ini bertujuan untuk mengisi kesenjangan dalam literatur dengan memperkenalkan salah satu cara menyusun model prakiraan kas pemerintah dengan tingkat akurasi yang memenuhi harapan Pengelola Kas pemerintah. Dengan menggunakan data historis harian pengeluaran pemerintah Indonesia sebagai sebuah studi kasus, penelitian ini menggunakan Autoregressive Integrated Moving Average (ARIMA) dan Jaringan Syaraf Tiruan (JST) untuk menyusun model prakiraan kas. Penelitian ini menunjukkan bahwa penggunaan metode Jaringan Syaraf Tiruan (JST) dapat menjadi alternatif dalam menyusun model prakiraan kas pemerintah dengan tingkat akurasi model prakiraan kas yang lebih tinggi dibandingkan menggunakan ARIMA model.

Deep learning-based container throughput forecasting: a triple bottom line approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sonali Shankar ◽  
Sushil Punia ◽  
P. Vigneswara Ilavarasan
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Moving Average ◽  
Short Term ◽  
Bottom Line ◽  
Content Type ◽  
Autoregressive Integrated Moving Average ◽  
Forecasting Method ◽  
Memory Network ◽  
Long Short Term Memory

PurposeContainer throughput forecasting plays a pivotal role in strategic, tactical and operational level decision-making. The determination and analysis of the influencing factors of container throughput are observed to enhance the predicting accuracy. Therefore, for effective port planning and management, this study employs a deep learning-based method to forecast the container throughput while considering the influence of economic, environmental and social factors on throughput forecasting.Design/methodology/approachA novel multivariate container throughput forecasting method is proposed using long short-term memory network (LSTM). The external factors influencing container throughput, delineated using triple bottom line, are considered as an input to the forecasting method. The principal component analysis (PCA) is employed to reduce the redundancy of the input variables. The container throughput data of the Port of Los Angeles (PLA) is considered for empirical analysis. The forecasting accuracy of the proposed method is measured via an error matrix. The accuracy of the results is further substantiated by the Diebold-Mariano statistical test.FindingsThe result of the proposed method is benchmarked with vector autoregression (VAR), autoregressive integrated moving average (ARIMAX) and LSTM. It is observed that the proposed method outperforms other counterpart methods. Though PCA was not an integral part of the forecasting process, it facilitated the prediction by means of “less data, more accuracy.”Originality/valueA novel deep learning-based forecasting method is proposed to predict container throughput using a hybridized autoregressive integrated moving average with external factors model and long short-term memory network (ARIMAX-LSTM).

Day-Ahead Solar Irradiance Forecasting in a Tropical Environment

2015 ◽  
Vol 137 (5) ◽  
Author(s):  
Aloysius W. Aryaputera ◽  
Dazhi Yang ◽  
Wilfred M. Walsh
Keyword(s):  
Solar Irradiance ◽  
Moving Average ◽  
Wrf Model ◽  
Tropical Environment ◽  
Weather Research And Forecasting ◽  
Tropical Regions ◽  
Autoregressive Integrated Moving Average ◽  
Seasonal Time Series ◽  
Using Data ◽  
Mean Square Errors

Day-ahead solar irradiance forecasting is carried out using data from a tropical environment, Singapore. The performance of the weather research and forecasting (WRF) model is evaluated. We explore various combinations of physics configuration setups in the WRF model and propose a setup for the tropical regions. The WRF model is benchmarked using persistence and two seasonal time series models, namely, the exponential smoothing (ETS) and seasonal autoregressive integrated moving average (SARIMA) models. It is shown that the WRF model outperforms the SARIMA model and achieves accuracies comparable with persistence and ETS models. Persistence, ETS, and WRF models have relative root mean square errors (rRMSE) of about 55–57%. Furthermore, we find that by combining the forecasting outputs of WRF and ETS models, errors can be reduced to 49%.

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