scholarly journals Data Mining Predictive Modeling for Prediction of Gold Prices Based on Dollar Exchange Rates, Bi Rates and World Crude Oil Prices

2019 ◽  
Vol 2 (2) ◽  
pp. 93
Author(s):  
Iman Priyadi ◽  
Julius Santony ◽  
Jufriadif Na'am
Keyword(s):  
Data Mining ◽  
Linear Regression ◽  
Crude Oil ◽  
Predictive Modeling ◽  
Regression Function ◽  
Gold Price ◽  
Linear Regression Function ◽  
Crude Oil Prices ◽  
Physical Form ◽  
Modeling Data

Gold is an investment instrument that is quite safe from inflationary attacks, and gold is one aspect of initiating investment. Can by buying gold in physical form and then selling when the price has risen high or by digitally investing gold. One of them is by trading gold online. To maximize the benefits of gold trading, a gold price prediction (XAUUSD) is needed for traders. This study aims to (1) Analyze various factors that influence the price of gold (2) Provide recommendations about the prediction of gold prices. Materials that will be used as objects of research to produce gold price predictions include historical XAUUSD (Gold) data itself, historical crude oil data, historical dollar data (USD IDR) and BI 7-Day Repo Rate (BI Rate). ), in producing the prediction of the gold price used Mining Predictive Modeling data using the linear regression function. The results to be achieved from this study is to provide accurate gold price predictions so that it can be used as a reference in making decisions to buy / sell positions in trading. The prediction of the XAUUSD gold price generated is expected to provide significant interest to the investment players (traders) in order to maximize the profit generated.From the results of the trading tests that have been carried out, the implementation of predictive modeling data mining using a linear regression function produces recommendations for gold price predictions (XAUUSD) with an accuracy of 85%.

Experimental Study of Short-Term Creep Characteristics Base on Step Loading-Unloading Method for Hard Rock

2013 ◽  
Vol 774-776 ◽  
pp. 86-93
Author(s):  
Fu Jun Zhang ◽  
Chuan Xiao Liu
Keyword(s):  
Linear Regression ◽  
Axial Strain ◽  
Radial Strain ◽  
Strain Curve ◽  
Regression Function ◽  
Nonlinear Creep ◽  
Linear Regression Function ◽  
Term Creep ◽  
Sandstone Specimen ◽  
Short Term Creep

Based on experimental results of uniaxial compression and short-term creep using 8-step loading-unloading method, fine sandstone specimen, which lower creep limit is 27MPa, present typical brittle breakage properties of hard rock. The correlative coefficients of linear regression function for isochronous stress-strain curve are all higher than 0. 92, and the ratio of long-term strength to instantaneous strength reaches 94. 39%，which indicate that the whole creep of fine sandstone specimen is weak. The average correlative coefficients of linear regression function for isochronous stress- axial strain curve are 3. 92% higher than that of average correlative coefficients of linear regression function for isochronous stress- radial strain curve, so nonlinear creep property of the fine sandstone specimen in axial direction is correspondingly weaker than that in radial direction. Negative Gauss distribution can be applied collectively to nonlinear creep of fine sandstone specimen, which has obvious time effect.With increasing loading, the reduction degrees of average correlative coefficients of linear fitting functions of isochronous stress-axial strain curve and isochronous stress-radial strain curve are 0. 97% and 0. 67% respectively, which indicates the linear correlation decreases commonly. Thus, the degree of nonlinear creep for fine sandstone specimen increases along with loading stress with obvious stress effect.

scholarly journals Temperature dependence of blood surface tension

2007 ◽  
pp. S93-S98
Author(s):  
J Rosina ◽  
E Kvašňák ◽  
D Šuta ◽  
H Kolářová ◽  
J Málek ◽  
...  
Keyword(s):  
Surface Tension ◽  
Linear Regression ◽  
Blood Serum ◽  
Whole Blood ◽  
Healthy Subjects ◽  
Regression Function ◽  
Sediment Surface ◽  
Linear Regression Equation ◽  
Linear Regression Function ◽  
Ring Method

Whole blood surface tension of 15 healthy subjects recorded by the ring method was investigated in the temperature range from 20 to 40 degrees C. The surface tension omega as a function of temperature t ( degrees C) is described by an equation of linear regression as omega(t) = (-0.473 t + 70.105) x 10(-3) N/m. Blood serum surface tension in the range from 20 to 40 degrees C is described by linear regression equation omega(t) = (-0.368 t + 66.072) x 10(-3) N/m and linear regression function of blood sediment surface tension is omega(t) = (-0.423 t + 67.223) x10(-3) N/m.

scholarly journals METODA ZANURZANIA REGRESJI W PRZYPADKU WYSTĘPOWANIA OBSERWACJI NIETYPOWYCH

2019 ◽  
Vol 20 (2) ◽  
pp. 83-92
Author(s):  
Małgorzata Kobylińska
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Least Squares Method ◽  
Regression Function ◽  
Maximum Depth ◽  
Two Dimensional ◽  
Structural Elements ◽  
Linear Regression Function ◽  
Regression Functions

This paper presents the application of the regression maximum depth for the estimation of linear regression function structural elements. For two-dimensional sets including untypical observations, regression functions were developed using the classical least squares method and a method based on the concept of observation depth measure in a sample. The effect of untypical observations on the estimated models has been noted.

scholarly journals Pengaruh inflasi, nilai tukar rupiah per dollar Amerika, harga minyak mentah dunia dan indeks harga saham gabungan terhadap harga emas di Indonesia

2020 ◽  
Vol 8 (2) ◽  
pp. 55-64
Author(s):  
Fadhel Kesarditama ◽  
Haryadi Haryadi ◽  
Yohanes Vyn Amzar
Keyword(s):  
Exchange Rate ◽  
Crude Oil ◽  
Stock Price ◽  
Price Index ◽  
Oil Prices ◽  
Gold Price ◽  
Positive Trend ◽  
Macroeconomic Variables ◽  
Crude Oil Prices ◽  
Stock Price Index

This study aims to analyze the trend of macroeconomic variables and gold prices in Indonesia and to determine the effect of macroeconomic variables on gold prices in Indonesia. This study uses a quantitative approach. The data used is secondary data from January 2014-December 2019. The analytical tools and techniques used are trend analysis with a linear trend approach and multiple linear regression models using the Ordinary Least Square method. The five research variables that were processed showed that there were differences in the direction of the data trend. Where the variables of Gold Price, Exchange Rate, and Composite Stock Price Index show a positive trend, while the variables of Inflation and World Crude Oil Prices show a negative trend. Furthermore, the variables of Exchange Rate, world Crude Oil Price, and Composite Stock Price Index show a positive and significant influence on the Gold Price in Indonesia. While the inflation variable shows a negative and significant effect on the Gold Price in Indonesia. Keywords: Inflation, foreign exchange,crude oil prices, idx composite and gold prices

Sign in / Sign up

Export Citation Format

Share Document