Also estimate the error (%) between the observed and estimated values through ARIMA model. This study will provide the estimates of suitable ARIMA model (0,1,2) along with Autocorrelation function (ACF) & Partia
4, No. 2, March 2016 Gold Price Forecasting Using ARIMA Model Banhi Guha and Gautam Bandyopadhyay Department of Management Studies, National Institute of Technology, Durgapur, India Email: banhi.guha@gmail.com, math_gb@yahoo.co.in Abstract—This study gives an inside view of the application ...
forecastinggold price changesadaptive network fuzzy inference systemDeveloping a precise and accurate model of gold price is critical to assets management because of its unique features. In this paper, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN) model have been ...
The MAE, MSE and RMSE of Prophet are also higher than the results of ARIMA model. Predicting The Price of Gold ETFs For The Next 2 Years Having validated our forecast results with our test data, we are going to perform an out of sample forecasting using the auto_arima meth...
Our results indicate that rolling ward networks exceed the recursive ward networks and feed forward networks in forecasting the sign variation in gold price. In addition, these results are compared to a traditional ARIMA (4, 1, 2) model, which was chosen using the Akaike information criterion (...
Financial institutions, investors, mining companies and related firms need an effective accurate forecasting model to examine gold price fluctuations in order to make correct decisions.This paper proposes an innovative approach to accurately forecast gold price movements and to interpret predictions. First,...
Gray economic model Oil price Oil crisis Peakoil 1. Introduction Different Crude oil prices are among the most important key variables that have a significant impact on the performance strategy of international financial markets [1]. Therefore, forecasting oil prices not only plays an effective role...
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This paper proposes a hybrid of linear autoregressive moving average (ARIMA) and a variant of non-linear generalized autoregressive conditional heteroscedasticity (GARCH) called GJR-GARCH in modeling and forecasting Malaysian gold price. In this study, the goodness of fit of the model is measured ...
Second, we explore whether uncertainty measures contribute to gold price forecasting. Our study is distinct from those that only examine the daily spot prices of gold (Shafiee, 2010; Dubey, 2016; Livieris, 2020). Livieris (2020) calls for outliers in the value of gold during certain future ...