An improved wavelet–ARIMA approach for forecasting metal prices

Kriechbaumer, Thomas; Angus, Andrew; Parsons, David; Casado, Mónica Rivas

doi:10.1016/j.resourpol.2013.10.005

Cited by 147 publications

(68 citation statements)

References 45 publications

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“…The ARIMA model could meet the subject's characteristics of self-similarity, periodicity, suddenness, and trends, delivering better forecasting performance in short-term forecasting [31]. So, this model has been applied broadly throughout other critical industries, such as public transport, metal prices, and the assessment of health care structures [32][33][34][35][36][37][38][39].…”

Section: Literature Reviewmentioning

confidence: 99%

Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

Jiang

Yang

2018

Sustainability

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Better prediction of energy demand is of vital importance for developing countries to develop effective energy strategies to improve energy security, partly because those countries' energy demands are increasing rapidly. In this work, metabolic grey model (MGM), autoregressive integrated moving average (ARIMA), MGM-ARIMA, and back propagation neural network (BP) are adopted to forecast energy demand in India, the third largest energy consumer in the world after China and the USA. The average relative errors between the actual and simulated value are 1.31% (MGM), 1.07%, 0.92% (MGM-ARIMA), and 0.39% (BP). The high prediction accuracy indicates that the prediction result is effective. The result shows that India's energy consumption will increase by 4.75% a year in the next 14 years. Compared with the 5.1% per year on average in 1995-2016, India's energy consumption will still continue its steady growth at about 5% growth from 2017 to 2030.

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Section: Literature Reviewmentioning

confidence: 99%

Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

Jiang

Yang

2018

Sustainability

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“…This was done in order to capture the specific features of the commodity market. For example, Kriechbaumer et al [40] joined a well-known ARIMA modeling with a wavelet decomposition approach to forecast metals' prices. The motivation behind using the wavelet analysis was to encompass in the model the cyclical behaviour of metals prices.…”

Section: Other Modelsmentioning

confidence: 99%

Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices

Drachal

2018

Sustainability

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Forecasting commodities prices on vividly changing markets is a hard problem to tackle. However, being able to determine important price predictors in a time-varying setting is crucial for sustainability initiatives. For example, the 2000s commodities boom gave rise to questioning whether commodities markets become over-financialized. In case of agricultural commodities, it was questioned if the speculative pressures increase food prices. Recently, some newly proposed Bayesian model combination scheme has been proposed, i.e., Dynamic Model Averaging (DMA). This method has already been applied with success in certain markets. It joins together uncertainty about the model and explanatory variables and a time-varying parameters approach. It can also capture structural breaks and respond to market disturbances. Secondly, it can deal with numerous explanatory variables in a data-rich environment. Similarly, like Bayesian Model Averaging (BMA), Dynamic Model Averaging (DMA), Dynamic Model Selection (DMS) and Median Probability Model (MED) start from Time-Varying Parameters' (TVP) regressions. All of these methods were applied to 69 spot commodities prices. The period between Dec 1983 and Oct 2017 was analysed. In approximately 80% of cases, according to the Diebold-Mariano test, DMA produced statistically significant more accurate forecast than benchmark forecasts (like the naive method or ARIMA). Moreover, amongst all the considered model types, DMA was in 22% of cases the most accurate one (significantly). MED was most often minimising the forecast errors (28%). However, in the text, it is clarified that this was due to some specific initial parameters setting. The second "best" model type was MED, meaning that, in the case of model selection, relying on the highest posterior probability is not always preferable.

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“…For commodities product, discrete wavelet transform (DWT) based method exists in forecasting of crude oil price [12], oil price [13], and natural gas price [14] that are the most interesting products in views of many researchers. There also has a work for forecasting metal prices that consists of aluminum, copper, lead, and zinc [15]. For our work, we investigate differently on two commodities products: gold price and rubber price.…”

Section: B Wavelet Transform In Commodities Price Time Series Forecamentioning

confidence: 99%

Multiresolution Analysis Based on Wavelet Transform for Commodity Prices Time Series Forecasting

Bunrit¹,

Kerdprasop²,

Kerdprasop³

2018

IJMLC

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Abstract-This research proposes time series forecasting of commodity prices by using multiresolution analysis from wavelet transform. In this work, discrete wavelet transform based multiresolution decomposition of Deubechies family is used. Firstly, Deubechies wavelet transform is applied to the training set of time series data up to level four. The reconstruction values of the approximation part of wavelet from each level are then used for the forecasting process by ARIMA model. The validation set of data is used to analyze and select the best model from all 4 levels of multiresolution decomposition. Finally, best selected validating model is used for evaluating the remaining testing data set. The forecasting results by using multiresolution analysis are compared to the case where the original data are directly modeled and forecasted by ARIMA. Results based on the mean absolute percentage error evaluation from using multiresolution analysis are better for both of the two studied data including daily gold price and rubber price. By applying multiresolution analysis, the improvement is 10.83% for gold price and 42.68% for rubber price. The variances of errors from the proposed method on both data sets are also much less than directly use the original time series data for forecasting.

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An improved wavelet–ARIMA approach for forecasting metal prices

Cited by 147 publications

References 45 publications

Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model

Some Novel Bayesian Model Combination Schemes: An Application to Commodities Prices

Multiresolution Analysis Based on Wavelet Transform for Commodity Prices Time Series Forecasting

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