Performance Estimation of ARIMA Model for Orographic Rainfall Region

2020 ◽  
Author(s):  
A. Pooja Verma ◽  
B. Swastika Chakraborty
Keyword(s):  
Arima Model ◽  
Performance Estimation ◽  
Orographic Rainfall ◽  
Rainfall Region

The Effects of Significant International Sports Events on Hungarian Suicide Rates

Crisis ◽  
2016 ◽  
Vol 37 (2) ◽  
pp. 148-154 ◽  
Author(s):  
Karoly Bozsonyi ◽  
Peter Osvath ◽  
Sandor Fekete ◽  
Lajos Bálint
Keyword(s):  
Significant Relationship ◽  
Olympic Games ◽  
Arima Model ◽  
Age Groups ◽  
The Elderly ◽  
Intervention Analysis ◽  
Middle Aged ◽  
Sports Events ◽  
Sport Events ◽  
The Olympic Games

Abstract. Background: Several studies found a significant relationship between important sport events and suicidal behavior. Aims: We set out to investigate whether there is a significant relationship between the raw suicide rate and the most important international sports events (Olympic Games, FIFA World Cup, UEFA European Championship) in such an achievement-oriented society as the Hungarian one, where these sport events receive great attention. Method: We examined suicide cases occurring over 15,706 days between January 1, 1970, and December 31, 2012 (43 years), separately for each gender. Because of the age-specific characteristics of suicide, the effects of these sport events were analyzed for the middle-aged (30–59 years old) and the elderly (over 60 years old) generations as well as for gender-specific population groups. The role of international sport events was examined with the help of time-series intervention analysis after cyclical and seasonal components were removed. Intervention analysis was based on the ARIMA model. Results: Our results showed that only the Olympic Games had a significant effect in the middle-aged population. Neither in the older male nor in any of the female age groups was a relationship between suicide and Olympic Games detected. Conclusion: The Olympic Games seem to decrease the rate of suicide among middle-aged men, slightly but significantly.

Performance Estimation of Charge Air Cooler for Passenger Car

2017 ◽  
Vol 11 (6) ◽  
pp. 414
Author(s):  
S. P. Gadewar ◽  
S. H. Gawande ◽  
S. A. Barhate
Keyword(s):  
Performance Estimation ◽  
Passenger Car ◽  
Air Cooler

Yield and quality evaluation of ginger (Zingiber officinale Rosc) genotypes under high rainfall zone of Tamil Nadu

1970 ◽  
pp. 95
Author(s):  
R Balakumbahan ◽  
J P Joshua
Keyword(s):  
Tamil Nadu ◽  
Quality Evaluation ◽  
Zingiber Officinale ◽  
Fibre Content ◽  
Research Station ◽  
Local Strains ◽  
High Rainfall ◽  
Yield And Quality ◽  
Rainfall Region ◽  
Horticultural Research

An experiment on ginger was undertaken between April 2012 to March 2015 at Horticultural Research Station, Pechiparai with an objective to identify suitable ginger cultivar or accession with higher yield and quality attributes suitable for high rainfall zone of Tamil Nadu. Twenty four ginger genotypes, local strains and varieties were collected from different ginger growing tracts of India and evaluated for their performance in high rainfall region. Among the twenty four genotypes tested, the accession Z. O- 4 recorded higher fresh rhizome yield (22.16 ha-1) than other genotypes whereas Z. O - 6 recorded highest dry recovery per cent (22.47%). Higher oleoresin and fibre content was recorded in genotypes Z. O – 5 (9.56%) and Z . O – 17 (11.20%) respectively. 

ANALISA PREDIKSI INDEKS HARGA SAHAM GABUNGAN MENGGUNAKAN METODE ARIMA

2020 ◽  
Vol 2 (2) ◽  
pp. 454
Author(s):  
Julkifli Purnama ◽  
Ahmad Juliana
Keyword(s):  
Capital Market ◽  
Stock Price ◽  
Time Series Data ◽  
Arima Model ◽  
Secondary Data ◽  
Actual Data ◽  
Series Data ◽  
Analysis Technique ◽  
The Right ◽  
The Capital Market

Investment in the capital market every manager needs to analyze to make decisions so that the right target to produce profits in accordance with what is expected. For that, we need a way to predict the decisions that will be taken in the future. The research objective is to find the best model and forecasting of the composite stock price index (CSPI). Data analysis technique The ARIMA Model time series data from historical data is the basis for forecasting. Secondary data is the closing price of the JCI on July 16 2018 to July 16 2019 to see how accurate the forecasting is done on the actual data at that time. The results of the study that the best Arima model is Arima 2.1.2 with an R-squared value of 0.014500, Schwarz criterion 10.83497 and Akaike info criterion of 10.77973. Results of forecasting actual data are 6394,609, dynamic forecast 6387,551 selisish -7,05799, statistics forecas 6400,653 difference of 6,043909. For investors or the public can use the ARIMA method to be able to predict or predict the capital market that will occur in the next period.

Model Statistika Prediksi Energi Surya Dengan Menggunakan Autoregresif Integrated Moving Average (ARIMA)

Jurnal MIPA ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 181
Author(s):  
Imriani Moroki ◽  
Alfrets Septy Wauran
Keyword(s):  
Renewable Energy ◽  
Solar Energy ◽  
Moving Average ◽  
Arima Model ◽  
Solar Irradiation ◽  
Energy Sources ◽  
Energy Potential ◽  
Good Design ◽  
Potential Sources ◽  
The Common

Energi terbarukan adalah salah satu masalah energi paling terkenal saat ini. Ada beberapa sumber potensial energi terbarukan. Salah satu energi terbarukan yang umum dan sederhana adalah energi matahari. Masalah besar ketersediaan energi saat ini adalah terbatasnya sumber energi konvensional seperti bahan bakar. Ini semua sumber energi memiliki banyak masalah karena memiliki jumlah energi yang terbatas. Penting untuk membuat model dan analisis berdasarkan ketersediaan sumber energi. Energi matahari adalah energi terbarukan yang paling disukai di negara-negara khatulistiwa saat ini. Tergantung pada produksi energi surya di daerah tertentu untuk memiliki desain dan analisis energi matahari yang baik. Untuk memiliki analisis yang baik tentang itu, dalam makalah ini kami membuat model prediksi energi surya berdasarkan data iradiasi matahari. Kami membuat model energi surya dan angin dengan menggunakan Metode Autoregresif Integrated Moving Average (ARIMA). Model ini diimplementasikan oleh R Studio yang kuat dari statistik. Sebagai hasil akhir, kami mendapatkan model statistik solar yang dibandingkan dengan data aktualRenewable energy is one of the most fomous issues of energy today. There are some renewable energy potential sources. One of the common n simple renewable energy is solar energy. The big problem of the availability of energy today is the limeted sources of conventional enery like fuel. This all energy sources have a lot of problem because it has a limited number of energy. It is important to make a model and analysis based on the availability of the energy sources. Solar energy is the most prefered renewable energy in equator countries today. It depends on the production of solar energy in certain area to have a good design and analysis of  the solar energy. To have a good analysis of it, in this paper we make a prediction model of solar energy based on the data of solar irradiation. We make the solar and wind enery model by using Autoregresif Integrated Moving Average (ARIMA) Method. This model is implemented by R Studio that is a powerfull of statistical. As the final result, we got the statistical model of solar comparing with the actual data

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