Forecasting Petroleum Fuel Price in Malaysia by ARIMA Model

2021 ◽  
pp. 671-678
Author(s):  
Rajalingam Sokkalingam ◽  
Richard M. N. Y. Sarpong-Streetor ◽  
Mahmod Othman ◽  
Hanita Daud ◽  
Derrick Asamoah Owusu
Keyword(s):  
Arima Model ◽  
Fuel Price ◽  
Petroleum Fuel

scholarly journals Forecasting Techniques for Sales Prediction

2020 ◽  
Vol 8 (5) ◽  
pp. 4924-4927
Keyword(s):  
Moving Average ◽  
Arima Model ◽  
Fuel Price ◽  
Common People ◽  
Forecasting Models ◽  
Sales Prediction ◽  
The Future ◽  
Feed Forward Neural Networks ◽  
Past Data ◽  
Predicted Values

All of us are very curious about future, very excited to know what will happen in the next moment. Similarly, retailers are also curious about the future of their business, its progress and their future sales. Walmart is the world’s biggest retailer and also has a vast grocery chain over the world. It was initially established in America 1962. In 2019, it has more than 11,000 stores in 28 countries but the sales differ from place to place. Many sales strategies, discount rates will be introduced for the improvement of sales. Retailers always try to attract the common people to visit their store. They always focus on improving the future sales. Using some Machine learning forecasting models, we can estimate the future sales based on the past data. Our aim is to apply time series forecasting models to retail sales data, which contains weekly sales of 45 Walmart stores across United States from 2010 to 2012. There are other factors which effects the analysis of weekly sales - markdown, consumer per index, Is Holiday (boolean value returns whether it is holiday or not), size of the store, unemployment, store type, fuel price and temperature. The forecasting models applied for the data are Autoregressive Integrated Moving Average (ARIMA) model and Feed Forward Neural Networks (FFNN). The dataset will be divided into training and testing datasets. The predicted values will be checked with the test data and accuracy will be calculated. Based on the accuracy we conclude which of the two models will better for the sales prediction.

scholarly journals A Proposed Petroleum Fuel Price Stabilization Plan

2009 ◽  
Author(s):  
Thomas Merrill ◽  
David M. Schizer
Keyword(s):  
Fuel Price ◽  
Price Stabilization ◽  
Petroleum Fuel

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.

Application of Box-Behnken Analysis on the Optimisation of Air Intake System for a Naturally Aspirated Engine

2020 ◽  
Vol 17 (2) ◽  
pp. 8029-8042
Author(s):  
N.S. Mustafa ◽  
N.H.A. Ngadiman ◽  
M.A. Abas ◽  
M.Y. Noordin
Keyword(s):  
Fuel Consumption ◽  
Fuel Efficiency ◽  
Engine Performance ◽  
Fuel Price ◽  
Design Expert ◽  
Intake System ◽  
Analysis Technique ◽  
System Components ◽  
Air Intake ◽  
High Influence

Fuel price crisis has caused people to demand a car that is having a low fuel consumption without compromising the engine performance. Designing a naturally aspirated engine which can enhance engine performance and fuel efficiency requires optimisation processes on air intake system components. Hence, this study intends to carry out the optimisation process on the air intake system and airbox geometry. The parameters that have high influence on the design of an airbox geometry was determined by using AVL Boost software which simulated the automobile engine. The optimisation of the parameters was done by using Design Expert which adopted the Box-Behnken analysis technique. The result that was obtained from the study are optimised diameter of inlet/snorkel, volume of airbox, diameter of throttle body and length of intake runner are 81.07 mm, 1.04 L, 44.63 mm and 425 mm, respectively. By using these parameters values, the maximum engine performance and minimum fuel consumption are 93.3732 Nm and 21.3695×10-4 kg/s, respectively. This study has fully accomplished its aim to determine the significant parameters that influenced the performance of airbox and optimised the parameters so that a high engine performance and fuel efficiency can be produced. The success of this study can contribute to a better design of an airbox.

Using ARIMA model for yield forecasting of important pulse crops of Odisha, India

2020 ◽  
Vol 4 (3) ◽  
pp. 646-659
Author(s):  
Abhiram Dash ◽  
Subrat Kumar Mahapatra
Keyword(s):  
Arima Model ◽  
Pulse Crops ◽  
Yield Forecasting

STUDI PENENTUAN KOMPOSISI OPTIMUM CAMPURAN BAHAN BAKAR BIODIESEL–PETRODIESEL

2018 ◽  
Vol 4 (2) ◽  
Author(s):  
Soni S. Wirawan dkk
Keyword(s):  
Carbon Monoxide ◽  
Particulate Matter ◽  
Diesel Engine ◽  
Fuel Consumption ◽  
Diesel Fuel ◽  
Cetane Number ◽  
Price Policy ◽  
Oxides Of Nitrogen ◽  
Fuel Price ◽  
Biodiesel Blend

Biodiesel is a viable substitute for petroleum-based diesel fuel. Its advantages are improved lubricity, higher cetane number and cleaner emission. Biodiesel and its blends with petroleum-based diesel fuel can be used in diesel engines without any signifi cant modifi cations to the engines. Data from the numerous research reports and test programs showed that as the percent of biodiesel in blends increases, emission of hydrocarbons (HC), carbon monoxide (CO), and particulate matter (PM) all decrease, but the amount of oxides of nitrogen (NOx) and fuel consumption is tend to increase. The most signifi cant hurdle for broader commercialization of biodiesel is its cost. In current fuel price policy in Indonesia (especially fuel for transportation), the higher percent of biodiesel in blend will increase the price of blends fuel. The objective of this study is to assess the optimum blends of biodiesel with petroleum-based diesel fuel from the technically and economically consideration. The study result recommends that 20% biodiesel blend with 80% petroleum-based diesel fuel (B20) is the optimum blend for unmodifi ed diesel engine uses.Keywords: biodiesel, emission, optimum, blend

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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