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Forecasting energy spot prices: A multiscale clustering recognition approach

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  • Li, Ranran

Abstract

Effective and timely forecasting of energy spot price plays a key role in the international bulk commodities market, which is related to the goal and direction of the reform of pricing mechanism. Previous studies are based on the decomposition-ensemble model to forecast the time series of high volatility in energy spot price, but ignore the adaptability of different models for different sub-series. This paper presents a hybrid forecasting model based on multiscale clustering recognition approach. Data preprocessing module is used to split the original series into several sub-series to reduce the complexity of the data, and then the optimal mode of input data is determined by feature selection. To identify the characteristic of sub-series, fuzzy cluster approach is introduced to distinguish with their complexity, which can divide adaptively them into different components with different frequency. This divide and conquer strategy give the reference for selecting suitable forecasting engine. With the help of tuned forecasting engines, these components are conducted respective prediction and the final results are reconfigured with the existing time point. The proposed model takes fully the advantages of different models into account, which ensures to use the data effectively. Through the empirical results, it demonstrates that the multiscale clustering recognition approach can both produce satisfactory prediction which is close to the actual values and be regarded as a promising tool for forecasting energy spot price.

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  • Li, Ranran, 2023. "Forecasting energy spot prices: A multiscale clustering recognition approach," Resources Policy, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:jrpoli:v:81:y:2023:i:c:s0301420723000284
    DOI: 10.1016/j.resourpol.2023.103320
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