Forecasting the spot price of gold:combined forecast approaches versus a composite forecast approach

Mui, H.W.; Chu, Chen‐Chin

doi:10.1080/02664769300000002

Cited by 6 publications

(2 citation statements)

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“…In general, mean absolute percentage error (MAPE), root mean square error (RMSE), mean absolute error (MAE), and maximum absolute percentage error (Max-APE) are the indicators used to evaluate the goodness of fit of predictive models [44]. Among these indicators, MAPE has become increasingly popular as a performance measure in forecasting [45][46][47], as it is easy to interpret and understand in addition to being highly reliable [48]. The coefficient of determination (R 2 ) was calculated for all data points by comparing the results predicted by the ANN model with the results obtained from laboratory tests.…”

Section: Validation Of the Selected Ann Modelmentioning

confidence: 99%

Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging

Lim

Cheon

Lee

et al. 2020

Sensors

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Soil water content is one of the most important physical indicators of landslide hazards. Therefore, quickly and non-destructively classifying soils and determining or predicting water content are essential tasks for the detection of landslide hazards. We investigated hyperspectral information in the visible and near-infrared regions (400–1000 nm) of 162 granite soil samples collected from Seoul (Republic of Korea). First, effective wavelengths were extracted from pre-processed spectral data using the successive projection algorithm to develop a classification model. A gray-level co-occurrence matrix was employed to extract textural variables, and a support vector machine was used to establish calibration models and the prediction model. The results show that an optimal correct classification rate of 89.8% could be achieved by combining data sets of effective wavelengths and texture features for modeling. Using the developed classification model, an artificial neural network (ANN) model for the prediction of soil water content was constructed. The input parameter was composed of Munsell soil color, area of reflectance (near-infrared), and dry unit weight. The accuracy in water content prediction of the developed ANN model was verified by a coefficient of determination and mean absolute percentage error of 0.91 and 10.1%, respectively.

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Section: Validation Of the Selected Ann Modelmentioning

confidence: 99%

Classification of Granite Soils and Prediction of Soil Water Content Using Hyperspectral Visible and Near-Infrared Imaging

Lim

Cheon

Lee

et al. 2020

Sensors

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“…In Australia, for example, Selvanathan (1991) compared the forecasted London daily gold prices from the Economic Research Centre with those from the ARIMA model, and proved that the ARIMA model is very low cost and effective enough to predict the price of gold. Mui and Chu (1993) employed a combined time series forecasting technique and a composite time series forecasting technique to predict the price of gold. They applied three weighting methods (the traditional equal weight [EW] method, the variancecovariance matrix [VC] method, the odds matrix [OM] method) in the combined forecasting modelling.…”

Section: Literature Reviewmentioning

confidence: 99%

Gold price forecasting with a neuro-fuzzy-based inference system

Makridou

Atsalakis

Zopounidis

et al. 2013

IJFERM

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Abstract:Following the importance of gold in the global economy and the high interest that has attracted recently, the objective of this paper is twofold: to predict the price of gold by using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and compare its forecasting accuracy with various time-series forecasting methods and the 'Buy and Hold' (B&H) strategy. The results show that the ANFIS's accuracy is far superior to the performance of all compared methods and therefore ANFIS demonstrates the potential of neuro fuzzy-based modelling for predicting the gold's price.

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