Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks

Jin, Junghwan

doi:10.1371/journal.pone.0142064

Cited by 40 publications

(18 citation statements)

References 36 publications

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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].…”

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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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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“…Nguyen and Nabney [24] showed that the combination of wavelet analysis and the neural network model would result in the improved electricity demand and natural gas forecasting accuracy [24]. Jin and Kim [25] forecasted the natural gas price using the combination of wavelet analysis and the neural network [25]. He et al [26] proposed a wavelet decomposed ensemble model for the crude oil market and found the improved performance of the proposed algorithm against the benchmark models [26].…”

Section: Multiscale Analysis In the Energy Marketsmentioning

confidence: 99%

Forecasting Energy Value at Risk Using Multiscale Dependence Based Methodology

Zha

Chen

et al. 2016

Entropy

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Abstract:In this paper, we propose a multiscale dependence-based methodology to analyze the dependence structure and to estimate the downside portfolio risk measures in the energy markets. More specifically, under this methodology, we formulate a new bivariate Empirical Mode Decomposition (EMD) copula based approach to analyze and model the multiscale dependence structure in the energy markets. The proposed model constructs the Copula-based dependence structure formulation in the Bivariate Empirical Mode Decomposition (BEMD)-based multiscale domain. Results from the empirical studies using the typical Australian electricity daily prices show that there exists a multiscale dependence structure between different regional markets across different scales. The proposed model taking into account the multiscale dependence structure demonstrates statistically significantly-improved performance in terms of accuracy and reliability measures.

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“…We still need to explore better forecasting models. Wavelet analysis has been used as a data preprocessing method and combined with other forecasting models in environmental science [27], hydrology [18] and financial time series [28]. It does not require stationarity of time series, which is often the basic requirement of traditional methods [11].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Hand-Foot-and-Mouth Disease Cases Using a Wavelet-based SARIMA-NNAR Hybrid Model

Feng

Shuang

et al. 2020

Preprint

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Background: Hand-foot-and-mouth disease(HFMD) is one of the most common diseases in children, which has high morbidity. Reliable forecasting is significant for prevention and control. Recently, hybrid models have been becoming popular and wavelet analysis has been widely used. Better prediction accuracy may be achieved with wavelet-based hybrid models. Thus, our aim is to forecast number of HFMD cases with wavelet-based hybrid models.Materials and methods: We fitted a wavelet-based SARIMA(seasonal autoregressive integrated moving average)-NNAR(neural network nonlinear autoregressive) hybrid model with HFMD weekly cases from 2009 to 2016 in Zhengzhou, China. At the same time, single SARIMA model, simplex NNAR model and pure SARIMA-NNAR hybrid model were established as well for comparison and estimation.Results: The wavelet-based SARIMA-NNAR hybrid model had an excellent performance whether in fitting or in forecasting compared to other models. Its fitted and forecasting time series were approximate to the actual observed time series.Conclusions: This wavelet-based SARIMA-NNAR hybrid model that we fitted is suitable for forecasting number of HFMD cases. It will facilitate prevention and control of HFMD.

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Forecasting Natural Gas Prices Using Wavelets, Time Series, and Artificial Neural Networks

Cited by 40 publications

References 36 publications

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

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

Forecasting Energy Value at Risk Using Multiscale Dependence Based Methodology

Forecasting Hand-Foot-and-Mouth Disease Cases Using a Wavelet-based SARIMA-NNAR Hybrid Model

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