Forecasting nonstationary time series based on Hilbert-Huang transform and machine learning

Kurbatsky, Victor; Sidorov, Denis; Spiryaev, Vadim; Tomin, Nikita

doi:10.1134/s0005117914050105

Cited by 20 publications

(16 citation statements)

References 19 publications

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“…Using a seasonal neural network approach for 72-h-ahead forecasting [22] presented a model with an overall RMSE of 1.46 for the period, which is more than four times greater than the simple linear model, but the forecasting period is also four times shorter than the forecasting period for the simple linear model. The artificial neural network approach provides a one-hour-ahead forecast with a MAE of 1.44 and an RMSE of 1.92 [17]. The simple linear model provides an in-sample MAE of −0.12 and out-of-sample MAE of −4.04.…”

Section: Discussionmentioning

confidence: 99%

“…The rapid growth in power production from renewable energy sources has increasingly urged producers to sell their electricity generation through the market, due to renewable intermittency. neural network performs best with a MAPE of 3.12%, a mean average error (MAE) of 1.44 and a root mean squared error (RMSE) of 1.92 [17]. This is generally 0.5-1% better than the support vector machine approach [20,21].…”

Section: Introductionmentioning

confidence: 97%

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High-Resolution Electricity Spot Price Forecast for the Danish Power Market

2020

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Energy markets with a high penetration of renewables are more likely to be challenged by price variations or volatility, which is partly due to the stochastic nature of renewable energy. The Danish electricity market (DK1) is a great example of such a market, as 49% of the power production in DK1 is based on wind power, conclusively challenging the electricity spot price forecast for the Danish power market. The energy industry and academia have tried to find the best practices for spot price forecasting in Denmark, by introducing everything from linear models to sophisticated machine-learning approaches. This paper presents a linear model for price forecasting—based on electricity consumption, thermal power production, wind production and previous electricity prices—to estimate long-term electricity prices in electricity markets with a high wind penetration levels, to help utilities and asset owners to develop risk management strategies and for asset valuation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

High-Resolution Electricity Spot Price Forecast for the Danish Power Market

2020

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“…ANN process the information like the neural network of the human brain, i.e., by connecting different simple units/nodes, named as artificial neurons, to form different complex networks [19]. Each node includes an activation function to produce an output value based on one or multiple inputs.…”

Section: Annmentioning

confidence: 99%

“…ANN has risen in importance since the 1980s and has been a research hotspot in the field of artificial intelligence. ANN has widespread applications in terms of data processing, classification, regression, function approximation, and numerical control.ANN process the information like the neural network of the human brain, i.e., by connecting different simple units/nodes, named as artificial neurons, to form different complex networks [19]. Each node includes an activation function to produce an output value based on one or multiple inputs.…”

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confidence: 99%

Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods

Zhang

Zhu

et al. 2019

Energies

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Natural gas has been proposed as a solution to increase the security of energy supply and reduce environmental pollution around the world. Being able to forecast natural gas price benefits various stakeholders and has become a very valuable tool for all market participants in competitive natural gas markets. Machine learning algorithms have gradually become popular tools for natural gas price forecasting. In this paper, we investigate data-driven predictive models for natural gas price forecasting based on common machine learning tools, i.e., artificial neural networks (ANN), support vector machines (SVM), gradient boosting machines (GBM), and Gaussian process regression (GPR). We harness the method of cross-validation for model training and monthly Henry Hub natural gas spot price data from January 2001 to October 2018 for evaluation. Results show that these four machine learning methods have different performance in predicting natural gas prices. However, overall ANN reveals better prediction performance compared with SVM, GBM, and GPR.Energies 2019, 12, 1680 2 of 17 usage of resources based on accurate predictions. Accurate natural gas price forecasting not only provides an important guide for effective implementation of energy policy and planning, but also is extremely significant in economic planning, energy investment, and environmental conservation. Therefore, researchers continue to study natural gas price forecasting models with great interest, with the aim of making predictions as accurate as possible in future.There are plenty of methods for analyzing and forecasting natural gas prices and machine learning is increasingly used. Machine learning algorithms can learn from historical relationships and trends in the data and make data-driven predictions or decisions. A great number of researchers have investigated natural gas price prediction with the aid of various machine learning methods so far. For instance, Abrishami and Varahrami mixed the data handling neural network technique with a rule-based expert system in forecasting natural gas prices [3]. Busse et al. used a nonlinear autoregressive exogenous model neural network [4]. Azadeh et al. studied a hybrid neuro-fuzzy method composed of ANN, fuzzy linear regression, and conventional regression [5]. Salehnia et al. developed several nonlinear models using the gamma test, including local linear regression, dynamic local linear regression, and ANN models [6]. Ceperic et al. proposed a strategic seasonality-adjusted, support vector regression machine-based model [7]. Su et al. utilized a least squares regression boosting algorithm in natural gas price prediction [8].As indicated in the abovementioned existing studies that exploited machine learning tools for natural gas price prediction, ANN and SVM are widely used machine learning methods in forecasting natural gas prices. In addition to ANN and SVM, this study will introduce two other common machine learning approaches, GBM and GPR (these two methods were used for forecasting hourly loads in US [9]). ...

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“…In this research, we introduced the method of EOFs to the MRD with the aid of the Hilbert transform. In the area of signal processing and machine-learning, the Hilbert transform (with empirical mode decomposition) was used to capture the dynamic property of time-series and to generate feature variables for prediction models [ 16 ]. It is thus expected that the proposed portfolio construction will outperform conventional portfolio constructions by including the dynamic properties of the risks, which are derived from the Hilbert transform of the returns of the assets.…”

Section: Introductionmentioning

confidence: 99%

Complex Valued Risk Diversification

Uchiyama

Kadoya

Nakagawa

2019

Entropy

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Risk diversification is one of the dominant concerns for portfolio managers. Various portfolio constructions have been proposed to minimize the risk of the portfolio under some constrains including expected returns. We propose a portfolio construction method that incorporates the complex valued principal component analysis into the risk diversification portfolio construction. The proposed method is verified to outperform the conventional risk parity and risk diversification portfolio constructions.

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Forecasting nonstationary time series based on Hilbert-Huang transform and machine learning

Cited by 20 publications

References 19 publications

High-Resolution Electricity Spot Price Forecast for the Danish Power Market

High-Resolution Electricity Spot Price Forecast for the Danish Power Market

Data Driven Natural Gas Spot Price Prediction Models Using Machine Learning Methods

Complex Valued Risk Diversification

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