Comparing Mean Squared Errors of X-12-ARIMA and Canonical ARIMA Model-Based Seasonal Adjustments

2012 ◽  
pp. 161-184
Author(s):  
William Bell ◽  
Yea-Jane Chu ◽  
George Tiao
Keyword(s):  
Arima Model ◽  
Mean Squared Errors ◽  
Model Based

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.

Overcoming Nonadmissibility in ARIMA-Model-Based Signal Extraction

2001 ◽  
Vol 19 (4) ◽  
pp. 455-464 ◽  
Author(s):  
Gabriele Fiorentini ◽  
Christophe Planas
Keyword(s):  
Arima Model ◽  
Signal Extraction ◽  
Model Based

scholarly journals Application of Wavelet Analysis Model Based on Hilbert Transform in Measles Outbreak Period

2018 ◽  
Vol 173 ◽  
pp. 03069
Author(s):  
Wang Tan ◽  
Qianqian Yang ◽  
Rencong Nie
Keyword(s):  
Wavelet Analysis ◽  
Hilbert Transform ◽  
Arima Model ◽  
Risk Level ◽  
Analysis Model ◽  
Time Dimension ◽  
Model Based ◽  
Outbreak Period ◽  
Medical Standards ◽  
The Future

Objective To study the trend of cycle activity of measles epidemic from 1950 to 2014 and establish a model to predict the national incidence of measles in the future. Methods Using the national measles monitoring data from 1950 to 2014, we establish a information database. Then, we set up the wavelet analysis model based on Hilbert transform to study the cycle of measles incidence. Finally, we establish the ARIMA model of measles risk level to predict the incidence of measles by SPSS software. Results Wavelet analysis shows that the outbreak cycle of the incidence of measles is getting longer in the time dimension. ARIMA model analysis shows that national incidence of measles will fluctuate and decline in the next 36 years, which is may related to the improvement of medical standards and people’s awareness of the measles prevention. Conclusions The national incidence of measles is declining. It is cyclical and its outbreak cycle is getting longer. Data shows that the incidence of measles will gradually decrease in the future, and gradually achieve the global goal of eliminating measles.

scholarly journals TIME SERIES FORECASTING BY USING A NEURAL ARIMA MODEL BASED ON WAVELET DECOMPOSITION

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Eliete Nascimento Pereira ◽  
Cassius Tadeu Scarpin ◽  
Luíz Albino Teixeira Júnior
Keyword(s):  
Time Series ◽  
Wavelet Decomposition ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Model Based

scholarly journals Unit Root Properties of Seasonal Adjustment and Related Filters: Special Cases

2017 ◽  
Vol 33 (1) ◽  
pp. 1-14 ◽  
Author(s):  
William.R. Bell
Keyword(s):  
Unit Root ◽  
Arima Model ◽  
Seasonal Adjustment ◽  
Arima Models ◽  
Linear Filters ◽  
Model Based ◽  
Adjustment Model ◽  
Special Cases ◽  
Component Models ◽  
Root Properties

Abstract Bell (2012) catalogued unit root factors contained in linear filters used in seasonal adjustment (model-based or from the X-11 method) but noted that, for model-based seasonal adjustment, special cases could arise where filters could contain more unit root factors than was indicated by the general results. This article reviews some special cases that occur with canonical ARIMA model based adjustment in which, with some commonly used ARIMA models, the symmetric seasonal filters contain two extra nonseasonal differences (i.e., they include an extra (1 - B)(1 - F)). This increases by two the degree of polynomials in time that are annihilated by the seasonal filter and reproduced by the seasonal adjustment filter. Other results for canonical ARIMA adjustment that are reported in Bell (2012), including properties of the trend and irregular filters, and properties of the asymmetric and finite filters, are unaltered in these special cases. Special cases for seasonal adjustment with structural ARIMA component models are also briefly discussed.

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