scholarly journals A Study of Seasonal ARIMA Model-Based Forecasting Method for Intelligent Food Control in a Livestock Environment

2018 ◽  
Vol 2 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Saraswathi Sivamani ◽  
◽  
Saravana Kumar Venkatesan ◽  
Keyword(s):  
Arima Model ◽  
Food Control ◽  
Model Based ◽  
Forecasting Method

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 MODELING AND FORECASTING OF INDIA’S DEFENSE EXPENDITURES USING BOX-JENKINS ARIMA MODEL

2021 ◽  
Vol 9 (2) ◽  
pp. 334-344
Author(s):  
Sapana Sharma ◽  
Sanju Karol
Keyword(s):  
Arima Model ◽  
Annual Data ◽  
Policy Makers ◽  
Rational Decision Making ◽  
Defense Expenditure ◽  
Forecasting Method ◽  
Modeling And Forecasting ◽  
One Step ◽  
The Government ◽  
The One

Many developed and developing countries are at the core of the security and peace agenda concerning rising defense expenditure and its enduring sustainability. The unremitting upsurge in defense expenditure pressurizes the government to rationally manage the resources so as to provide security and peace services in the most efficient, effective and equitable way. It is necessary to forecast the defense expenditure in India which leads the policy makers to execute reforms in order to detract burdens on these resources, as well as introduce appropriate plan strategies on the basis of rational decision making for the issues that may arise. The purpose of this study is to investigate the appropriate type of model based on the Box–Jenkins methodology to forecast defense expenditure in India. The present study applies the one-step ahead forecasting method for annual data over the period 1961 to 2020. The results show that ARIMA (1,1,1) model with static forecasting being the most appropriate to forecast the India’s defense expenditure.

Basketball sports neural network model based on nonlinear classification

2020 ◽  
pp. 1-10
Author(s):  
Rongkai Duan ◽  
Pu Sun
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Linear Part ◽  
Arima Model ◽  
Research Field ◽  
Modeling And Analysis ◽  
Model Based ◽  
Bodily Injury ◽  
Nonlinear Classification

With the continuous innovation of science and technology, the mathematical modeling and analysis of bodily injury in the process of exercise have always been a hot and difficult point in the research field of scholars. Although there are many research results on the nonlinear classification of the basketball sports neural network model, usually only one model is used, which has certain defects. The combination forecasting model based on the ARIMA model and neural network based on LSTM can make up for this defect. In the process of the experiment, the most important is the construction of the combination model and the acquisition of volunteer data in the process of the ball game. In this experiment, the ARIMA model is used as the linear part of the data, and LSTM neural network model is used to get the sequence of body injury. The results of the empirical study show that: it is reasonable to divide the injury of thigh and calf in the process of basketball sports, which is very consistent with the force point of the human body in the process of sports. The results of the two models predicting the average degree of bodily injury for many times are about 0.32 and 0.38 respectively, which are far less than 1. The execution time of the program for simultaneous prediction on the computer is about 1 minute, which is extremely effective.

scholarly journals The ARIMA Box-Jenkins Method has been used to Predict the Price of Large Curly Red Chilis

2021 ◽  
pp. 89
Author(s):  
Yustirania Septiani ◽  
Vinca Ayu Setyowati
Keyword(s):  
Arima Model ◽  
Economic Value ◽  
Price Forecasting ◽  
Market Demand ◽  
Model Based

Chili is one of the potential commodities based on market demand and high economic value. The price of chili has fluctuated every month so that this commodity contributes to inflation in food that can affect overall general inflation. Thus, an analysis of forecasting prices for large curly red chili is needed so thar people and farmers do not need to worry and can prepare for future risks. Price forecasting in this study uses the Box-Jenkins ARIMA method. The data used is the price of lare curly red chili prices from December 2015 to April 2020. The data to be analyzed is then made into several forms of the ARIMA model and one will be chosen as the best ARIMA model. Based on the results of the study, ARIMA (1,1,3) is the best model. Thus the forecast results obtained for the price of large curly red chili in Magelang City from May 2020 to February 2021. With this research it is expected ti be able to assist the Depasrtment of Industry and Trade of Magelang City in making decisions related to the price of lare curly red chilli which fluctuates every year.

scholarly journals Rainfall Data Modeling in Simalungun Regency Using the Arima Box-Jenkins Method

2021 ◽  
Vol 10 (1) ◽  
pp. 21-27
Author(s):  
Desi Fransiska D
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Arima Model ◽  
Average Error ◽  
Mean Square ◽  
Plant Cultivation ◽  
Forecasting Method ◽  
The Right ◽  
Error Percentage

One of the components of the environment that determines the success of plant cultivation is climate. To predict rainfall, the author uses the ARIMA Box Jenkins method, which is a quantitative forecasting method. The data used are data for the period July 2012 to June 2017. In this study, the right model is the ARIMA model (2,0,2) with Xt = 4.05668 + 0.9416Xt-1 - 1.0039Xt-2 - 0, 8558et-1 + 0.9617et-2 + et which is used to forecast rainfall for the next 12 periods. The selection is based on the smallest MSE (average error squared) value of 0.033401954 and the smallest RMSE (root mean square error value), which is 0.001115691 and the smallest MAPE (absolute average error percentage) is -0 , 00801773.

scholarly journals Forecasting of Air Passengers using ARIMA Modeling

2019 ◽  
Vol 8 (11S) ◽  
pp. 1050-1054
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Autocorrelation Function ◽  
Arima Model ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Forecasting Method ◽  
Partial Autocorrelation Function ◽  
Time Series Method ◽  
Arima Modeling

Air passengers prediction is said to be the centre of gravity of the growth. With people on the move constantly, there is bound to be some dissatisfaction amongst the customers which could be due to various reason, varying from overbooking of flights to ground operations. This dissatisfaction can be controlled till a limit, in ballpark figuring. In the past, this has been done using various machine learning techniques. For this prediction, in this project, ARIMA Modeling is used which is a time series forecasting method, based on machine learning. To test the stationarity of the data, which is done using Dickey Fuller test. If the data is stationary, it is fit into the ARIMA Model. If the data isn’t stationary, it is made stationary by differencing or by logarithmic transformation. The logarithmic method to make the data stationary. Once the data is stationary, using the Partial autocorrelation function and the autocorrelation function, values of p and q are found, which are required in the time series method. These values are then fit into the ARIMA Modeling and hence, the results are predicted. Upon the use and fitting of various models, the ARIMA(2,1,2) has been the best fit, having the least RMS and RMSE values.

Properties of Weighted Geometric Means Combination Forecasting Model Based on Absolute of Grey Incidence

2012 ◽  
Vol 490-495 ◽  
pp. 442-446 ◽  
Author(s):  
Li Hong Sun
Keyword(s):  
Forecasting Model ◽  
Sufficient Condition ◽  
Redundant Information ◽  
Geometric Means ◽  
Model Based ◽  
Combination Forecasting ◽  
Forecasting Method ◽  
Combination Forecasting Model

weighted geometric means combination forecasting is a kind of nonlinear combination forecasting model. Based on absolute of grey incidence, a weighted geometric means combination forecasting model is proposed. Superior combination forecasting, dominant forecasting method and redundant degree are put forward. Under certain conditions the sufficient condition of existence of non-inferior combination and superior combination forecasting are discussed, redundant information is pointed out in a judging theorem.

Sign in / Sign up

Export Citation Format

Share Document