Pemodelan Harga Emas Dunia Menggunakan Metode Nonparametrik Polinomial Lokal Dilengkapi Gui R

Abstract

Investing in gold is a flexible choice because it can be sold at any time and used as an emergency fund. Investors should have the knowledge to predict data from time to time to achieve investment goals. One of the statistical methods for time series data modeling is ARIMA. The ARIMA model is strict with the assumptions that the data must be stationary, the residuals must be normally distributed, independent, and with constant variance, so an alternative model is proposed, namely nonparametric regression model, which has no modeling assumptions requirement. In this study, the daily world gold price data will be modeled using a local polynomial nonparametric model as an alternative because the assumptions in the ARIMA are not fulfilled. The data is divided into 2 parts, namely in sample data from January 2, 2020 to November 30, 2020 to form a model and out sample data from December 1, 2020 to December 31, 2020 used for evauation of model performance based on MAPE values. The chosen best model is the local polynomial model with Gaussian kernel function of degree 5, bandwidth of 373, and local point of 1744 with an MSE value of 482.6420. The local polynomial model out sample data MAPE value is 0.61%, indicating that the model has excellent forecasting capability. In this study, Graphical User Interface (GUI) using R software with the help of shiny package is also built, making data analyzing easier and generating more interactive display output.

Comparison of Local Polynomial Regression and Arima in Predicting the Number of Foreign Tourist Visits to Indonesia

“…One of the nonparametric regression methods that can be used is the estimator [6]. Estimation of the local polynomial regression model can use WLS (Weighted Least Square) so that weights are needed.…”

“…One method that can be used for prediction is local polynomial regression. The advantage of a local polynomial is its adaptability to data that divides the data into certain regions and then estimates the predetermined regions [6]. In addition to using the local polynomial regression approach, this research also makes predictions using the Autoregressive Integrated Moving Average (ARIMA) method.…”

Pemodelan Data Time Series Menggunakan Pendekatan Regresi Polinomial Lokal Pada Data Harga Saham MDKA

“…Oleh sebab itu, diperlukan pemodelan alternatif yang lebih fleksibel tanpa memerlukan adanya asumsi residual tersebut yaitu model nonparametrik dan salah satunya adalah model regresi polinomial lokal. Suparti dan Prahutama [2] dan Hendrian et al [3] telah memodelkan data time series yaitu data pemakaian beban listrik Kota Semarang dan harga emas dunia menggunakan regresi polinomial lokal. Penelitian tersebut tidak melibatkan sifat stasioneritas data dan tidak melakukan pengukuran terhadap kebaikan model, sehingga metode tersebut masih perlu dilakukan penelitian lebih lanjut.…”

Gold Price Fluctuation Forecasting Based on Newton and Lagrange Polynomial Interpolation

“…Gold price fluctuations can affect investment and trading decisions in global financial markets (Sravani et al, 2021). Therefore, forecasting gold price fluctuations is important to make the right investment decisions and minimize the risk of loss (Hendrian et al, 2021). To help forecast gold price fluctuations, various math-based forecasting methods have been developed.…”

“…Meanwhile, there are articles that discuss machine learning algorithms, approaches, and techniques (Pragna et al, 2022;Radhamani et al, 2022;ul Sami and Junero, 2017;Tripurana et al, 2022) that applied machine learning technique to forecast financial financial indicators, with a primary focus on forecasting the price of gold. In another research, discuss the forecasting of gold prices using Generalized Autoregressive Conditional Heteroscedasticity (Garch) model (Haris, 2020); local polynomial nonparametric method equipped with GUI R (Hendrian et al, 2021); average-based fuzzy time series method (Hariwijaya et al, 2020); multiple linear regression method (Sravani et al, 2021); Nearest Neighbor Retrieval method (Nugroho, 2018); data mining techniques (Mahena et al, 2015). These research explores gold price forecasting in the context of making investment decisions in gold stocks.…”