On parameter estimation for Malaysian gold prices modelling and forecasting

Miswan, Nor Hamizah; Ping, Pung Yean; Ahmad, Mohd Hamdan

doi:10.12988/ijma.2013.13106

Cited by 12 publications

(6 citation statements)

References 4 publications

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“…ML and LS were compared in [32] to obtain an ARIMA model to predict the gold price. The results reported an error of 0.81% and 2.86% when using a LS and a ML, respectively.…”

Section: Autoregressive Integrated Moving Average Processesmentioning

confidence: 99%

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

et al. 2015

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Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting. Although classical statistical-based methods provides reasonably good results, the result of the application of data mining outperforms those of classical ones. Hence, this work faces two main challenges: (i) to provide a compact mathematical formulation of the mainly used techniques; (ii) to review the latest works of time series forecasting and, as case study, those related to electricity price and demand markets.

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“…ML and LS were compared in [32] to obtain an ARIMA model to predict the gold price. The results reported an error of 0.81% and 2.86% when using a LS and a ML, respectively.…”

Section: Autoregressive Integrated Moving Average Processesmentioning

confidence: 99%

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

et al. 2015

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“…The autoregressive integrated moving average (ARIMA) is one of the Box-Jenkins model that is widely applied in research practice for gold price modeling and forecasting, either as a benchmark, comparison, hybrid or forecasting models [4][5][6] [3]. However, a recent study in gold price reported that there is a strong positive trend from 2002 to 2011 that is associated with a higher volatility in that period [7].…”

Section: Introductionmentioning

confidence: 99%

Modelling gold price using ARIMA–TGARCH

Roslindar¹,

Azlinna²,

Hura³

et al. 2016

ams

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Statistical models can be used to characterize numerical data so as to understand its behavior and pattern. Gold price model, for example, can give signals to investors as to when they should enter and/or exit the market. To find an appropriate gold price model, it is crucial to choose a model that reflects the pattern of the price movement so as to make the model fit and adequate. This study examines the performances of ARIMA-TGARCH with five innovations in modeling and forecasting gold prices. The innovations considered include Gaussian, Student's-t, skewed Student's-t, generalized error distribution and skewed generalized error distribution. Using daily gold price data from the years 2003 to 2014, this study concluded that a hybrid ARIMA(0,1,0)-TGARCH(1,1) with t-innovation was the best model due to the existence of leverage effect and heavier tail characteristics in the data.

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“…An accuracy of the forecasting model is useful to reduce any risk in strategic planning. A forecasting is not only a model [2,4] that employs statistics to formulate the model [5,6,7] such as ARIMA and Holt-Winters but it is also a model that employs machine learning algorithm [3,7] to formulate the model such as Artificial Neural Networks (ANNs) or Support Vector Machine (SVM). An advantage of machine learning model is flexible and convenient for using.…”

Section: Introductionmentioning

confidence: 99%

Agricultural product forecasting using machine learning approach

Sujjaviriyasup¹,

Pitiruek²

2013

ijma

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In this study we develop the machine learning models for forecasting agricultural products. The main concept of building the models is because machine learning is flexible and convenient to implement and it can be potential applications for a naïve user. The proposed model of Support Vector Machine (SVM) is able to forecast nonlinear or linear forecasting function upon kernel function. Many experiments were performed on the development of SVM and the most precision model by using statistical criteria was also selected. Real data of Thailand's Pacific white shrimp export and Thailand's produced chicken were used to validate candidate models. Autoregressive Integrate Moving Average (ARIMA) is also selected as a benchmarking to compare other developed models. For Pacific white shrimp export case, comparing to ARIMA, the error reduction from MAE, RMSE, and MAPE is 25.76%, 18.11%, and 19.05%, respectively. Moreover, the error reduction from MAE, RMSE, and MAPE is 21.78%, 18.76%, and 18.11%, respectively, for the case of produced chicken.

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On parameter estimation for Malaysian gold prices modelling and forecasting

Cited by 12 publications

References 4 publications

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting

Modelling gold price using ARIMA–TGARCH

Agricultural product forecasting using machine learning approach

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